Veröffentlichungen - Institut Informatik

anthropomorphic robot arm anthropomorphism arm movement model artificial skin attractor dynamics approach Autonomous robotics BCI behavior generation direct physical interaction Dynamical systems force sensing haptic interface human anatomy human motor behavior human operators intuition inverse kinematics Machine Learning man machine interaction man-machine-interaction manipulators mechanical structure modeling movement model object recognition operators gesture pervasive design robot manipulator control robot vision robotic assistant sensory channels user modelling 
Zeige alle
80 Einträge « ‹ 1 von 2 › » 
2023
80.
 Ali, Omair;  Saif-ur-Rehman, Muhammad;  Glasmachers, Tobias;  Iossifidis, Ioannis;  Klaes, Christian
ConTraNet: A Hybrid Network for Improving the Classification of EEG and EMG Signals with Limited Training Data Artikel 
In: Computers in Biology and Medicine, S. 107649, 2023, ISSN: 0010-4825.
Abstract | Links | BibTeX | Schlagwörter: BCI, Brain computer interface, Deep learning, EEG decoding, EMG decoding, Machine Learning
@article{aliConTraNetHybridNetwork2023,

title = {ConTraNet: A Hybrid Network for Improving the Classification of EEG and EMG Signals with Limited Training Data},

author = {Omair Ali and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},

url = {https://www.sciencedirect.com/science/article/pii/S0010482523011149},

doi = {10.1016/j.compbiomed.2023.107649},

issn = {0010-4825},

year  = {2023},

date = {2023-11-02},

urldate = {2023-11-02},

journal = {Computers in Biology and Medicine},

pages = {107649},

abstract = {Objective Bio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely used for the rehabilitation of physically disabled people and for the characterization of cognitive impairments. Successful decoding of these bio-signals is however non-trivial because of the time-varying and non-stationary characteristics. Furthermore, existence of short- and long-range dependencies in these time-series signal makes the decoding even more challenging. State-of-the-art studies proposed Convolutional Neural Networks (CNNs) based architectures for the classification of these bio-signals, which are proven useful to learn spatial representations. However, CNNs because of the fixed size convolutional kernels and shared weights pay only uniform attention and are also suboptimal in learning short-long term dependencies, simultaneously, which could be pivotal in decoding EEG and EMG signals. Therefore, it is important to address these limitations of CNNs. To learn short- and long-range dependencies simultaneously and to pay more attention to more relevant part of the input signal, Transformer neural network-based architectures can play a significant role. Nonetheless, it requires a large corpus of training data. However, EEG and EMG decoding studies produce limited amount of the data. Therefore, using standalone transformers neural networks produce ordinary results. In this study, we ask a question whether we can fix the limitations of CNN and transformer neural networks and provide a robust and generalized model that can simultaneously learn spatial patterns, long-short term dependencies, pay variable amount of attention to time-varying non-stationary input signal with limited training data. Approach In this work, we introduce a novel single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that contains the strengths of both CNN and Transformer neural networks. ConTraNet uses a CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the short- and long-range or global dependencies in the signal and learn to pay different attention to different parts of the signals. Main results We evaluated and compared the ConTraNet with state-of-the-art methods on four publicly available datasets (BCI Competition IV dataset 2b, Physionet MI-EEG dataset, Mendeley sEMG dataset, Mendeley sEMG V1 dataset) which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, 7-class, and 10-class decoding tasks). Significance With limited training data ConTraNet significantly improves classification performance on four publicly available datasets for 2, 3, 4, 7, and 10-classes compared to its counterparts.},

keywords = {BCI, Brain computer interface, Deep learning, EEG decoding, EMG decoding, Machine Learning},

pubstate = {published},

tppubtype = {article}

}

Schließen
Objective Bio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely used for the rehabilitation of physically disabled people and for the characterization of cognitive impairments. Successful decoding of these bio-signals is however non-trivial because of the time-varying and non-stationary characteristics. Furthermore, existence of short- and long-range dependencies in these time-series signal makes the decoding even more challenging. State-of-the-art studies proposed Convolutional Neural Networks (CNNs) based architectures for the classification of these bio-signals, which are proven useful to learn spatial representations. However, CNNs because of the fixed size convolutional kernels and shared weights pay only uniform attention and are also suboptimal in learning short-long term dependencies, simultaneously, which could be pivotal in decoding EEG and EMG signals. Therefore, it is important to address these limitations of CNNs. To learn short- and long-range dependencies simultaneously and to pay more attention to more relevant part of the input signal, Transformer neural network-based architectures can play a significant role. Nonetheless, it requires a large corpus of training data. However, EEG and EMG decoding studies produce limited amount of the data. Therefore, using standalone transformers neural networks produce ordinary results. In this study, we ask a question whether we can fix the limitations of CNN and transformer neural networks and provide a robust and generalized model that can simultaneously learn spatial patterns, long-short term dependencies, pay variable amount of attention to time-varying non-stationary input signal with limited training data. Approach In this work, we introduce a novel single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that contains the strengths of both CNN and Transformer neural networks. ConTraNet uses a CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the short- and long-range or global dependencies in the signal and learn to pay different attention to different parts of the signals. Main results We evaluated and compared the ConTraNet with state-of-the-art methods on four publicly available datasets (BCI Competition IV dataset 2b, Physionet MI-EEG dataset, Mendeley sEMG dataset, Mendeley sEMG V1 dataset) which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, 7-class, and 10-class decoding tasks). Significance With limited training data ConTraNet significantly improves classification performance on four publicly available datasets for 2, 3, 4, 7, and 10-classes compared to its counterparts.
Schließen
https://www.sciencedirect.com/science/article/pii/S0010482523011149
doi:10.1016/j.compbiomed.2023.107649
Schließen
79.
 Fidencio, Aline Xavier;  Klaes, Christian;  Iossifidis, Ioannis
Exploring Error-related Potentials in Adaptive Brain-Machine Interfaces: Challenges and Investigation of Occurrence and Detection Ratios Proceedings Article 
In: BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2023.
Abstract | BibTeX | Schlagwörter: BCI, EEG, Machine Learning
@inproceedings{xavierfidencioExploringErrorrelatedPotentials2023,

title = {Exploring Error-related Potentials in Adaptive Brain-Machine Interfaces: Challenges and Investigation of Occurrence and Detection Ratios},

author = {Aline Xavier Fidencio and Christian Klaes and Ioannis Iossifidis},

year  = {2023},

date = {2023-09-15},

urldate = {2023-09-15},

booktitle = {BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},

publisher = {BCCN Bernstein Network Computational Network},

abstract = {Non-invasive techniques like EEG can record error-related potentials (ErrPs), neural signals associated with error processing and awareness. ErrPs are generated in response to self-made and external errors, including those produced by the BMI. Since ErrPs are implicitly elicited and don’t add extra workload for the subject, they serve as a natural and intrinsic feedback source for developing adaptive BMIs. In our study, we assess the occurrence of interaction ErrPs in an adaptive BMI that combines ErrPs and reinforcement learning. We intentionally provoke ErrPs when the BMI misinterprets the user’s intention and performs an incorrect action. Subjects participated in a game controlled by a keyboard and/or motor imagery (imagining hand movements), and EEG data were recorded using an eight-electrode gel-based EEG system. Results reveal that obtaining a distinct ErrPs signal for each subject is more challenging than anticipated. Current practices report the ErrP in terms of over all subjects and trials difference grand average (error minus correct). This approach has, however, the limitation of masking the inter-trial and subject variability, which are relevant for the online single-trial detection of such signals. Moreover, the reported ErrPs waveshape exhibit differences in terms of components observed, as well as their respective latencies, even when very similar tasks are used. Consequently, we conducted additional individualized data analysis to gain deeper insights into the single-trial nature of the ErrPs. As a result, we determined the need for a better understanding and further investigation of how effectively the ErrPs waveforms generalize across subjects, tasks, experimental protocols, and feedback modalities. Given the challenges in obtaining a clear signal for all subjects and the limitations found in existing literature (Xavier Fidêncio et al., 2022), we hypothesize whether an error signal measurable at the scalp level is consistently generated when subjects encounter erroneous conditions. To address this question, we will assess the occurrence-to-detection ratio of ErrPs using invasive and non-invasive recording techniques, examining how uncertainties regarding error generation in the brain impact the learning pipeline.},

keywords = {BCI, EEG, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
Non-invasive techniques like EEG can record error-related potentials (ErrPs), neural signals associated with error processing and awareness. ErrPs are generated in response to self-made and external errors, including those produced by the BMI. Since ErrPs are implicitly elicited and don’t add extra workload for the subject, they serve as a natural and intrinsic feedback source for developing adaptive BMIs. In our study, we assess the occurrence of interaction ErrPs in an adaptive BMI that combines ErrPs and reinforcement learning. We intentionally provoke ErrPs when the BMI misinterprets the user’s intention and performs an incorrect action. Subjects participated in a game controlled by a keyboard and/or motor imagery (imagining hand movements), and EEG data were recorded using an eight-electrode gel-based EEG system. Results reveal that obtaining a distinct ErrPs signal for each subject is more challenging than anticipated. Current practices report the ErrP in terms of over all subjects and trials difference grand average (error minus correct). This approach has, however, the limitation of masking the inter-trial and subject variability, which are relevant for the online single-trial detection of such signals. Moreover, the reported ErrPs waveshape exhibit differences in terms of components observed, as well as their respective latencies, even when very similar tasks are used. Consequently, we conducted additional individualized data analysis to gain deeper insights into the single-trial nature of the ErrPs. As a result, we determined the need for a better understanding and further investigation of how effectively the ErrPs waveforms generalize across subjects, tasks, experimental protocols, and feedback modalities. Given the challenges in obtaining a clear signal for all subjects and the limitations found in existing literature (Xavier Fidêncio et al., 2022), we hypothesize whether an error signal measurable at the scalp level is consistently generated when subjects encounter erroneous conditions. To address this question, we will assess the occurrence-to-detection ratio of ErrPs using invasive and non-invasive recording techniques, examining how uncertainties regarding error generation in the brain impact the learning pipeline.
Schließen
78.
 Grün, Felix;  Iossifidis, Ioannis
Investigation of the Interplay of Model-Based and Model-Free Learning Using Reinforcement Learning Proceedings Article 
In: BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2023.
Abstract | BibTeX | Schlagwörter: Machine Learning, Reinforcement learning
@inproceedings{grunInvestigationInterplayModelBased2023,

title = {Investigation of the Interplay of Model-Based and Model-Free Learning Using Reinforcement Learning},

author = {Felix Grün and Ioannis Iossifidis},

year  = {2023},

date = {2023-09-15},

urldate = {2023-09-15},

booktitle = {BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},

publisher = {BCCN Bernstein Network Computational Network},

abstract = {The reward prediction error hypothesis of dopamine in the brain states that activity of dopaminergic neurons in certain brain regions correlates with the reward prediction error that corresponds to the temporal difference error, often used as a learning signal in model free reinforcement learning (RL). This suggests that some form of reinforcement learning is used in animal and human brains when learning a task. On the other hand, it is clear that humans are capable of building an internal model of a task, or environment, and using it for planning, especially in sequential tasks. In RL, these two learning approaches, model-driven and reward-driven, are known as model based and model-free RL approaches. Both systems were previously thought to exist in parallel, with some higher process choosing which to use. A decade ago, research suggested both could be used concurrently, with some subject-specific weight assigned to each [1]. Still, the prevalent belief appeared to be that model-free learning is the default mechanism used, replaced or assisted by model-based planning only when the task demands it, i.e. higher rewards justify the additional cognitive effort. Recently, Feher da Silva et al. [2] questioned this belief, presenting data and analyses that indicate model-based learning may be used on its own and can even be computationally more efficient. We take a RL perspective, consider different ways to combine model-based and model-free approaches for modeling and for performance, and discuss how to further study this interplay in human behavioral experiments.},

keywords = {Machine Learning, Reinforcement learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
The reward prediction error hypothesis of dopamine in the brain states that activity of dopaminergic neurons in certain brain regions correlates with the reward prediction error that corresponds to the temporal difference error, often used as a learning signal in model free reinforcement learning (RL). This suggests that some form of reinforcement learning is used in animal and human brains when learning a task. On the other hand, it is clear that humans are capable of building an internal model of a task, or environment, and using it for planning, especially in sequential tasks. In RL, these two learning approaches, model-driven and reward-driven, are known as model based and model-free RL approaches. Both systems were previously thought to exist in parallel, with some higher process choosing which to use. A decade ago, research suggested both could be used concurrently, with some subject-specific weight assigned to each [1]. Still, the prevalent belief appeared to be that model-free learning is the default mechanism used, replaced or assisted by model-based planning only when the task demands it, i.e. higher rewards justify the additional cognitive effort. Recently, Feher da Silva et al. [2] questioned this belief, presenting data and analyses that indicate model-based learning may be used on its own and can even be computationally more efficient. We take a RL perspective, consider different ways to combine model-based and model-free approaches for modeling and for performance, and discuss how to further study this interplay in human behavioral experiments.
Schließen
77.
 Schmidt, Marie Dominique;  Iossifidis, Ioannis
The Link between Muscle Activity and Upper Limb Kinematics Proceedings Article 
In: BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2023.
Abstract | BibTeX | Schlagwörter: BCI, Machine Learning
@inproceedings{schmidtLinkMuscleActivity2023,

title = {The Link between Muscle Activity and Upper Limb Kinematics},

author = {Marie Dominique Schmidt and Ioannis Iossifidis},

year  = {2023},

date = {2023-09-15},

urldate = {2023-09-15},

booktitle = {BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},

publisher = {BCCN Bernstein Network Computational Network},

abstract = {The upper limbs are crucial in performing daily tasks that require strength, a wide range of motion, and precision. To achieve coordinated motion, planning and timing are critical. Sensory information about the target and the current body state is essential, as well as integrating past experiences, represented by pre-learned inverse dynamics that generate associated muscle activity. We propose a generative model that predicts upper limb muscle activity from a variety of simple and complex everyday motions by means of a recurrent neural network. The model shows promising results, with a good fit for different subjects and abstracts well for new motions. We handle the high inter-subject variation in muscle activity using a transfer learning approach, resulting in a good fit for new subjects. Our approach has implications for fundamental movement control understanding and the rehabilitation of neuromuscular diseases using myoelectric prostheses and functional electrical stimulation. Our model can efficiently predict both muscle activity and motion trajectory, which can assist in developing more effective rehabilitation techniques.},

keywords = {BCI, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
The upper limbs are crucial in performing daily tasks that require strength, a wide range of motion, and precision. To achieve coordinated motion, planning and timing are critical. Sensory information about the target and the current body state is essential, as well as integrating past experiences, represented by pre-learned inverse dynamics that generate associated muscle activity. We propose a generative model that predicts upper limb muscle activity from a variety of simple and complex everyday motions by means of a recurrent neural network. The model shows promising results, with a good fit for different subjects and abstracts well for new motions. We handle the high inter-subject variation in muscle activity using a transfer learning approach, resulting in a good fit for new subjects. Our approach has implications for fundamental movement control understanding and the rehabilitation of neuromuscular diseases using myoelectric prostheses and functional electrical stimulation. Our model can efficiently predict both muscle activity and motion trajectory, which can assist in developing more effective rehabilitation techniques.
Schließen
76.
 Schmidt, Marie D.;  Glasmachers, Tobias;  Iossifidis, Ioannis
The Concepts of Muscle Activity Generation Driven by Upper Limb Kinematics Artikel 
In: BioMedical Engineering OnLine, Bd. 22, Nr. 1, S. 63, 2023, ISSN: 1475-925X.
Abstract | Links | BibTeX | Schlagwörter: Artificial generated signal, BCI, Electromyography (EMG), Generative model, Inertial measurement unit (IMU), Machine Learning, Motion parameters, Muscle activity, Neural networks, transfer learning, Voluntary movement
@article{schmidtConceptsMuscleActivity2023,

title = {The Concepts of Muscle Activity Generation Driven by Upper Limb Kinematics},

author = {Marie D. Schmidt and Tobias Glasmachers and Ioannis Iossifidis},

url = {https://doi.org/10.1186/s12938-023-01116-9},

doi = {10.1186/s12938-023-01116-9},

issn = {1475-925X},

year  = {2023},

date = {2023-06-24},

urldate = {2023-06-24},

journal = {BioMedical Engineering OnLine},

volume = {22},

number = {1},

pages = {63},

abstract = {The underlying motivation of this work is to demonstrate that artificial muscle activity of known and unknown motion can be generated based on motion parameters, such as angular position, acceleration, and velocity of each joint (or the end-effector instead), which are similarly represented in our brains. This model is motivated by the known motion planning process in the central nervous system. That process incorporates the current body state from sensory systems and previous experiences, which might be represented as pre-learned inverse dynamics that generate associated muscle activity.},

keywords = {Artificial generated signal, BCI, Electromyography (EMG), Generative model, Inertial measurement unit (IMU), Machine Learning, Motion parameters, Muscle activity, Neural networks, transfer learning, Voluntary movement},

pubstate = {published},

tppubtype = {article}

}

Schließen
The underlying motivation of this work is to demonstrate that artificial muscle activity of known and unknown motion can be generated based on motion parameters, such as angular position, acceleration, and velocity of each joint (or the end-effector instead), which are similarly represented in our brains. This model is motivated by the known motion planning process in the central nervous system. That process incorporates the current body state from sensory systems and previous experiences, which might be represented as pre-learned inverse dynamics that generate associated muscle activity.
Schließen
https://doi.org/10.1186/s12938-023-01116-9
doi:10.1186/s12938-023-01116-9
Schließen
75.
 Saif-ur-Rehman, Muhammad;  Ali, Omair;  Klaes, Christian;  Iossifidis, Ioannis
Adaptive SpikeDeep-Classifier: Self-organizing and self-supervised machine learning algorithm for online spike sorting Artikel 
In: arXiv:2304.01355 [cs, math, q-bio], 2023.
Links | BibTeX | Schlagwörter: BCI, Machine Learning, Spike Sorting
@article{saifurrehman2023adaptive,

title = {Adaptive SpikeDeep-Classifier: Self-organizing and self-supervised machine learning algorithm for online spike sorting},

author = {Muhammad Saif-ur-Rehman and Omair Ali and Christian Klaes and Ioannis Iossifidis},

doi = {10.48550/arXiv.2304.01355},

year  = {2023},

date = {2023-05-02},

urldate = {2023-05-02},

journal = {arXiv:2304.01355 [cs, math, q-bio]},

keywords = {BCI, Machine Learning, Spike Sorting},

pubstate = {published},

tppubtype = {article}

}

Schließen
doi:10.48550/arXiv.2304.01355
Schließen
2022
74.
 Grün, Felix;  Saif-ur-Rehman, Muhammad;  Glasmachers, Tobias;  Iossifidis, Ioannis
Invariance to Quantile Selection in Distributional Continuous Control Artikel 
In: arXiv:2212.14262 [cs.LG], 2022.
Links | BibTeX | Schlagwörter: Artificial Intelligence (cs.AI), FOS: Computer and information sciences, I.2.6, I.2.8, Machine Learning, Machine Learning (cs.LG)
@article{grunInvarianceQuantileSelection2022,

title = {Invariance to Quantile Selection in Distributional Continuous Control},

author = {Felix Grün and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},

url = {https://arxiv.org/abs/2212.14262},

doi = {10.48550/ARXIV.2212.14262},

year  = {2022},

date = {2022-12-29},

urldate = {2022-12-29},

journal = {arXiv:2212.14262 [cs.LG]},

keywords = {Artificial Intelligence (cs.AI), FOS: Computer and information sciences, I.2.6, I.2.8, Machine Learning, Machine Learning (cs.LG)},

pubstate = {published},

tppubtype = {article}

}

Schließen
https://arxiv.org/abs/2212.14262
doi:10.48550/ARXIV.2212.14262
Schließen
73.
 Lehmler, Stephan Johann;  Saif-ur-Rehman, Muhammad;  Glasmachers, Tobias;  Iossifidis, Ioannis
Deep transfer learning compared to subject-specific models for sEMG decoders Artikel 
In: Journal of Neural Engineering, Bd. 19, Nr. 5, 2022.
Abstract | Links | BibTeX | Schlagwörter: BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning, transfer learning
@article{lehmlerTransferLearningPatientSpecific2021bb,

title = {Deep transfer learning compared to subject-specific models for sEMG decoders},

author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},

editor = {{IOP Publishing},

url = {https://dx.doi.org/10.1088/1741-2552/ac9860},

doi = {10.1088/1741-2552/ac9860},

year  = {2022},

date = {2022-10-28},

urldate = {2022-10-28},

journal = {Journal of Neural Engineering},

volume = {19},

number = {5},

abstract = {{Objective. Accurate decoding of surface electromyography (sEMG) is pivotal for muscle-to-machine-interfaces and their application e.g. rehabilitation therapy. sEMG signals have high inter-subject variability, due to various factors, including skin thickness, body fat percentage, and electrode placement. Deep learning algorithms require long training time and tend to overfit if only few samples are available. In this study, we aim to investigate methods to calibrate deep learning models to a new user when only a limited amount of training data is available. Approach. Two methods are commonly used in the literature, subject-specific modeling and transfer learning. In this study, we investigate the effectiveness of transfer learning using weight initialization for recalibration of two different pretrained deep learning models on new subjects data and compare their performance to subject-specific models. We evaluate two models on three publicly available databases (non invasive adaptive prosthetics database 2–4) and compare the performance of both calibration schemes in terms of accuracy, required training data, and calibration time. Main results. On average over all settings, our transfer learning approach improves 5%-points on the pretrained models without fine-tuning, and 12%-points on the subject-specific models, while being trained for 22% fewer epochs on average. Our results indicate that transfer learning enables faster learning on fewer training samples than user-specific models. Significance. To the best of our knowledge, this is the first comparison of subject-specific modeling and transfer learning. These approaches are ubiquitously used in the field of sEMG decoding. But the lack of comparative studies until now made it difficult for scientists to assess appropriate calibration schemes. Our results guide engineers evaluating similar use cases.},

keywords = {BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning, transfer learning},

pubstate = {published},

tppubtype = {article}

}

Schließen
{Objective. Accurate decoding of surface electromyography (sEMG) is pivotal for muscle-to-machine-interfaces and their application e.g. rehabilitation therapy. sEMG signals have high inter-subject variability, due to various factors, including skin thickness, body fat percentage, and electrode placement. Deep learning algorithms require long training time and tend to overfit if only few samples are available. In this study, we aim to investigate methods to calibrate deep learning models to a new user when only a limited amount of training data is available. Approach. Two methods are commonly used in the literature, subject-specific modeling and transfer learning. In this study, we investigate the effectiveness of transfer learning using weight initialization for recalibration of two different pretrained deep learning models on new subjects data and compare their performance to subject-specific models. We evaluate two models on three publicly available databases (non invasive adaptive prosthetics database 2–4) and compare the performance of both calibration schemes in terms of accuracy, required training data, and calibration time. Main results. On average over all settings, our transfer learning approach improves 5%-points on the pretrained models without fine-tuning, and 12%-points on the subject-specific models, while being trained for 22% fewer epochs on average. Our results indicate that transfer learning enables faster learning on fewer training samples than user-specific models. Significance. To the best of our knowledge, this is the first comparison of subject-specific modeling and transfer learning. These approaches are ubiquitously used in the field of sEMG decoding. But the lack of comparative studies until now made it difficult for scientists to assess appropriate calibration schemes. Our results guide engineers evaluating similar use cases.
Schließen
https://dx.doi.org/10.1088/1741-2552/ac9860
Schließen
72.
 Grün, Felix;  Iossifidis, Ioannis
Exploring Distribution Parameterizations for Distributional Continuous Control Proceedings Article 
In: BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2022.
Links | BibTeX | Schlagwörter: Machine Learning, Reinforcement learning
@inproceedings{grunExploringDistributionParameterizations2022,

title = {Exploring Distribution Parameterizations for Distributional Continuous Control},

author = {Felix Grün and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2022.112},

year  = {2022},

date = {2022-09-15},

urldate = {2022-09-15},

booktitle = {BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},

publisher = {BCCN Bernstein Network Computational Network},

keywords = {Machine Learning, Reinforcement learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2022.112
Schließen
71.
 Lehmler, Stephan Johann;  Saif-ur-Rehman, Muhammad;  Iossifidis, Ioannis
Modeling Subject Specfic Surface EMG Features by Means of Deep Learning Proceedings Article 
In: BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2022.
Links | BibTeX | Schlagwörter: BCI, Machine Learning
@inproceedings{lehmlerModelingSubjectSpecfic2022,

title = {Modeling Subject Specfic Surface EMG Features by Means of Deep Learning},

author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2022.309},

year  = {2022},

date = {2022-09-15},

urldate = {2022-09-15},

booktitle = {BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},

publisher = {BCCN Bernstein Network Computational Network},

keywords = {BCI, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2022.309
Schließen
70.
 Schmidt, Marie Dominique;  Iossifidis, Ioannis
Linking Muscle Activity and Motion Trajectory Proceedings Article 
In: BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2022.
Links | BibTeX | Schlagwörter: BCI, Machine Learning
@inproceedings{schmidtLinkingMuscleActivity2022,

title = {Linking Muscle Activity and Motion Trajectory},

author = {Marie Dominique Schmidt and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2022.191},

year  = {2022},

date = {2022-09-15},

urldate = {2022-09-15},

booktitle = {BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},

publisher = {BCCN Bernstein Network Computational Network},

keywords = {BCI, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2022.191
Schließen
69.
 Sziburis, Tim;  Blex, Susanne;  Iossifidis, Ioannis
A Dataset of 3D Hand Transport Trajectories Determined by Inertial Measurements from a Single Sensor Proceedings Article 
In: BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2022.
Links | BibTeX | Schlagwörter: BCI, Machine Learning
@inproceedings{sziburisDataset3DHand2022,

title = {A Dataset of 3D Hand Transport Trajectories Determined by Inertial Measurements from a Single Sensor},

author = {Tim Sziburis and Susanne Blex and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2022.186},

year  = {2022},

date = {2022-09-15},

urldate = {2022-09-15},

booktitle = {BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},

publisher = {BCCN Bernstein Network Computational Network},

keywords = {BCI, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2022.186
Schließen
68.
 Fidencio, Aline Xavier;  Klaes, Christian;  Iossifidis, Ioannis
Closed-Loop Adaptation of Brain-Machine Interfaces Using Error-Related Potentials and Reinforcement Learning Proceedings Article 
In: BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2022.
Links | BibTeX | Schlagwörter: BCI, Machine Learning
@inproceedings{xavierfidencioClosedloopAdaptationBrainmachine2022,

title = {Closed-Loop Adaptation of Brain-Machine Interfaces Using Error-Related Potentials and Reinforcement Learning},

author = {Aline Xavier Fidencio and Christian Klaes and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2022.136},

year  = {2022},

date = {2022-09-15},

urldate = {2022-09-15},

booktitle = {BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},

publisher = {BCCN Bernstein Network Computational Network},

keywords = {BCI, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2022.136
Schließen
67.
 Fidencio, Aline Xavier;  Klaes, Christian;  Iossifidis, Ioannis
Error-Related Potentials in Reinforcement Learning-Based Brain-Machine Interfaces Artikel 
In: Frontiers in Human Neuroscience, Bd. 16, 2022.
Abstract | Links | BibTeX | Schlagwörter: BCI, EEG, error-related potentials, Machine Learning, Reinforcement learning
@article{xavierfidencioErrorrelated,

title = {Error-Related Potentials in Reinforcement Learning-Based Brain-Machine Interfaces},

author = {Aline Xavier Fidencio and Christian Klaes and Ioannis Iossifidis},

url = {https://www.frontiersin.org/article/10.3389/fnhum.2022.806517},

doi = {https://doi.org/10.3389/fnhum.2022.806517},

year  = {2022},

date = {2022-06-24},

urldate = {2022-06-24},

journal = {Frontiers in Human Neuroscience},

volume = {16},

abstract = {The human brain has been an object of extensive investigation in different fields. While several studies have focused on understanding the neural correlates of error processing, advances in brain-machine interface systems using non-invasive techniques further enabled the use of the measured signals in different applications. The possibility of detecting these error-related potentials (ErrPs) under different experimental setups on a single-trial basis has further increased interest in their integration in closed-loop settings to improve system performance, for example, by performing error correction. Fewer works have, however, aimed at reducing future mistakes or learning. We present a review focused on the current literature using non-invasive systems that have combined the ErrPs information specifically in a reinforcement learning framework to go beyond error correction and have used these signals for learning.},

keywords = {BCI, EEG, error-related potentials, Machine Learning, Reinforcement learning},

pubstate = {published},

tppubtype = {article}

}

Schließen
The human brain has been an object of extensive investigation in different fields. While several studies have focused on understanding the neural correlates of error processing, advances in brain-machine interface systems using non-invasive techniques further enabled the use of the measured signals in different applications. The possibility of detecting these error-related potentials (ErrPs) under different experimental setups on a single-trial basis has further increased interest in their integration in closed-loop settings to improve system performance, for example, by performing error correction. Fewer works have, however, aimed at reducing future mistakes or learning. We present a review focused on the current literature using non-invasive systems that have combined the ErrPs information specifically in a reinforcement learning framework to go beyond error correction and have used these signals for learning.
Schließen
https://www.frontiersin.org/article/10.3389/fnhum.2022.806517
doi:https://doi.org/10.3389/fnhum.2022.806517
Schließen
66.
 Ali, Omair;  Saif-ur-Rehman, Muhammad;  Glasmachers, Tobias;  Iossifidis, Ioannis;  Klaes, Christian
ConTraNet: A Single End-to-End Hybrid Network for EEG-based and EMG-based Human Machine Interfaces Artikel 
In: 2022.
Abstract | Links | BibTeX | Schlagwörter: BCI, Machine Learning, neural processing, signal processing
@article{aliConTraNetSingleEndtoend2022,

title = {ConTraNet: A Single End-to-End Hybrid Network for EEG-based and EMG-based Human Machine Interfaces},

author = {Omair Ali and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},

url = {http://arxiv.org/abs/2206.10677},

doi = {10.48550/arXiv.2206.10677},

year  = {2022},

date = {2022-06-21},

urldate = {2022-06-21},

abstract = {Objective: Electroencephalography (EEG) and electromyography (EMG) are two non-invasive bio-signals, which are widely used in human machine interface (HMI) technologies (EEG-HMI and EMG-HMI paradigm) for the rehabilitation of physically disabled people. Successful decoding of EEG and EMG signals into respective control command is a pivotal step in the rehabilitation process. Recently, several Convolutional neural networks (CNNs) based architectures are proposed that directly map the raw time-series signal into decision space and the process of meaningful features extraction and classification are performed simultaneously. However, these networks are tailored to the learn the expected characteristics of the given bio-signal and are limited to single paradigm. In this work, we addressed the question that can we build a single architecture which is able to learn distinct features from different HMI paradigms and still successfully classify them. Approach: In this work, we introduce a single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that is equally useful for EEG-HMI and EMG-HMI paradigms. ConTraNet uses CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the long-range dependencies in the signal, which are crucial for the classification of EEG and EMG signals. Main results: We evaluated and compared the ConTraNet with state-of-the-art methods on three publicly available datasets which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, and 10-class decoding tasks). Significance: The results suggest that ConTraNet is robust to learn distinct features from different HMI paradigms and generalizes well as compared to the current state of the art algorithms.},

keywords = {BCI, Machine Learning, neural processing, signal processing},

pubstate = {published},

tppubtype = {article}

}

Schließen
Objective: Electroencephalography (EEG) and electromyography (EMG) are two non-invasive bio-signals, which are widely used in human machine interface (HMI) technologies (EEG-HMI and EMG-HMI paradigm) for the rehabilitation of physically disabled people. Successful decoding of EEG and EMG signals into respective control command is a pivotal step in the rehabilitation process. Recently, several Convolutional neural networks (CNNs) based architectures are proposed that directly map the raw time-series signal into decision space and the process of meaningful features extraction and classification are performed simultaneously. However, these networks are tailored to the learn the expected characteristics of the given bio-signal and are limited to single paradigm. In this work, we addressed the question that can we build a single architecture which is able to learn distinct features from different HMI paradigms and still successfully classify them. Approach: In this work, we introduce a single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that is equally useful for EEG-HMI and EMG-HMI paradigms. ConTraNet uses CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the long-range dependencies in the signal, which are crucial for the classification of EEG and EMG signals. Main results: We evaluated and compared the ConTraNet with state-of-the-art methods on three publicly available datasets which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, and 10-class decoding tasks). Significance: The results suggest that ConTraNet is robust to learn distinct features from different HMI paradigms and generalizes well as compared to the current state of the art algorithms.
Schließen
http://arxiv.org/abs/2206.10677
doi:10.48550/arXiv.2206.10677
Schließen
65.
 Ali, Omair;  Saif-ur-Rehman, Muhammad;  Dyck, Susanne;  Glasmachers, Tobias;  Iossifidis, Ioannis;  Klaes, Christian
Enhancing the decoding accuracy of EEG signals by the introduction of anchored-STFT and adversarial data augmentation method Artikel 
In: Nature Scientific Reports, Bd. 12, Ausg. 1, S. 4245, 2022, ISSN: 2045-2322.
Abstract | Links | BibTeX | Schlagwörter: Adversarial NN, BCI, computer science, EEG, Machine Learning, Quantitative Biology, Quantitative Methods
@article{aliAnchoredSTFTGNAAExtension2021a,

title = {Enhancing the decoding accuracy of EEG signals by the introduction of anchored-STFT and adversarial data augmentation method},

author = {Omair Ali and Muhammad Saif-ur-Rehman and Susanne Dyck and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},

url = {https://www.nature.com/articles/s41598-022-07992-w},

doi = {https://doi.org/10.1038/s41598-022-07992-w},

issn = {2045-2322},

year  = {2022},

date = {2022-03-10},

urldate = {2022-03-10},

journal = {Nature Scientific Reports},

volume = {12},

issue = {1},

pages = {4245},

abstract = {Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one of the most used brain signals in non-invasive BCI applications but are often contaminated with noise. Therefore, it is possible that meaningful patterns for classifying EEG signals are deeply hidden. State-of-the-art deep-learning algorithms are successful in learning hidden, meaningful patterns. However, the quality and the quantity of the presented inputs is pivotal. Here, we propose a novel feature extraction method called anchored Short Time Fourier Transform (anchored-STFT), which is an advanced version of STFT, as it minimizes the trade-off between temporal and spectral resolution presented by STFT. In addition, we propose a novel augmentation method, called gradient norm adversarial augmentation (GNAA). GNAA is not only an augmentation method but is also used to harness adversarial inputs in EEG data, which not only improves the classification accuracy but also enhances the robustness of the classifier. In addition, we also propose a new CNN architecture, namely Skip-Net, for the classification of EEG signals. The proposed pipeline outperforms all state-of-the-art methods and yields an average classification accuracy of 90.7 % and 89.54 % on BCI competition II dataset III and BCI competition IV dataset 2b, respectively.},

keywords = {Adversarial NN, BCI, computer science, EEG, Machine Learning, Quantitative Biology, Quantitative Methods},

pubstate = {published},

tppubtype = {article}

}

Schließen
Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one of the most used brain signals in non-invasive BCI applications but are often contaminated with noise. Therefore, it is possible that meaningful patterns for classifying EEG signals are deeply hidden. State-of-the-art deep-learning algorithms are successful in learning hidden, meaningful patterns. However, the quality and the quantity of the presented inputs is pivotal. Here, we propose a novel feature extraction method called anchored Short Time Fourier Transform (anchored-STFT), which is an advanced version of STFT, as it minimizes the trade-off between temporal and spectral resolution presented by STFT. In addition, we propose a novel augmentation method, called gradient norm adversarial augmentation (GNAA). GNAA is not only an augmentation method but is also used to harness adversarial inputs in EEG data, which not only improves the classification accuracy but also enhances the robustness of the classifier. In addition, we also propose a new CNN architecture, namely Skip-Net, for the classification of EEG signals. The proposed pipeline outperforms all state-of-the-art methods and yields an average classification accuracy of 90.7 % and 89.54 % on BCI competition II dataset III and BCI competition IV dataset 2b, respectively.
Schließen
https://www.nature.com/articles/s41598-022-07992-w
doi:https://doi.org/10.1038/s41598-022-07992-w
Schließen
64.
 Schmidt, Marie Dominique;  Glasmachers, Tobias;  Iossifidis, Ioannis
From Motion to Muscle Artikel 
In: arXiv: 2201.11501 [cs.LG], 2022.
Links | BibTeX | Schlagwörter: BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network
@article{schmidt2022motion,

title = {From Motion to Muscle},

author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},

doi = {https://doi.org/10.48550/arXiv.2201.11501},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {arXiv: 2201.11501 [cs.LG]},

keywords = {BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network},

pubstate = {published},

tppubtype = {article}

}

Schließen
doi:https://doi.org/10.48550/arXiv.2201.11501
Schließen
63.
 Fidencio, Aline Xavier;  Glasmachers, Tobias;  Iossifidis, Ioannis
Error-Related Potentials Detection with Dry- and Wet-Electrode EEG Proceedings Article 
In: FENS, Forum 2022, FENS, Federation of European Neuroscience Societies, 2022.
Abstract | BibTeX | Schlagwörter: BCI, EEG, error-related potentials, Machine Learning
@inproceedings{fidencioErrorrelatedPotentialsDetection2022,

title = {Error-Related Potentials Detection with Dry- and Wet-Electrode EEG},

author = {Aline Xavier Fidencio and Tobias Glasmachers and Ioannis Iossifidis},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {FENS, Forum 2022},

publisher = {FENS, Federation of European Neuroscience Societies},

abstract = {Electroencephalography (EEG) is a non-invasive technique for measuring brain electrical activity from electrodes placed on the scalp surface. Improvements in this technology are particularly relevant because they also boost brain-machine interfaces (BMI) development. Commonly, gel-based electrodes are used since they guarantee a high-quality signal. Alternatively, dry electrodes have been introduced, more suitable for daily use. In this work, we compare conventional dry and wet electrode systems specifically for the detection of error-related potentials (ErrPs). ErrPs are elicited as a reaction to both self-made and external errors. There has been increased interest in the integration of these signals into BMIs to improve their performance since they provide a convenient source of feedback to the system with no extra workload for the subject. These signals can be used, e.g., to correct errors or even for system adaptation. ErrP-based BMIs in the literature have consistently used wet electrodes. Therefore, even though both electrodes types have been compared for other event-related potentials (e.g., P300), it is relevant to know whether the signal quality for the detection of ErrPs is comparable among them. In this work, we implement a simple game to elicit ErrPs and compare the quality of the measured signals. We tested the feasibility of the experimental protocol to elicit ErrP and the measured ErrP displayed a similar waveshape in terms of observed peaks. However, differences exist in both latencies as well as in their amplitude. These variations and other relevant characteristics have to be further verified with more subjects},

keywords = {BCI, EEG, error-related potentials, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
Electroencephalography (EEG) is a non-invasive technique for measuring brain electrical activity from electrodes placed on the scalp surface. Improvements in this technology are particularly relevant because they also boost brain-machine interfaces (BMI) development. Commonly, gel-based electrodes are used since they guarantee a high-quality signal. Alternatively, dry electrodes have been introduced, more suitable for daily use. In this work, we compare conventional dry and wet electrode systems specifically for the detection of error-related potentials (ErrPs). ErrPs are elicited as a reaction to both self-made and external errors. There has been increased interest in the integration of these signals into BMIs to improve their performance since they provide a convenient source of feedback to the system with no extra workload for the subject. These signals can be used, e.g., to correct errors or even for system adaptation. ErrP-based BMIs in the literature have consistently used wet electrodes. Therefore, even though both electrodes types have been compared for other event-related potentials (e.g., P300), it is relevant to know whether the signal quality for the detection of ErrPs is comparable among them. In this work, we implement a simple game to elicit ErrPs and compare the quality of the measured signals. We tested the feasibility of the experimental protocol to elicit ErrP and the measured ErrP displayed a similar waveshape in terms of observed peaks. However, differences exist in both latencies as well as in their amplitude. These variations and other relevant characteristics have to be further verified with more subjects
Schließen
62.
 Schmidt, Marie Dominique;  Glasmachers, Tobias;  Iossifidis, Ioannis
Motion Intention Prediction Proceedings Article 
In: FENS, Forum 2022, FENS, Federation of European Neuroscience Societies, 2022.
Abstract | BibTeX | Schlagwörter: BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network
@inproceedings{schmidtMotionIntentionPrediction2022a,

title = {Motion Intention Prediction},

author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {FENS, Forum 2022},

publisher = {FENS, Federation of European Neuroscience Societies},

abstract = {Motion intention prediction is the key to robot-assisted rehabilitation systems. These can rely on various biological signals. One commonly used signal is the muscle activity measured by an electromyogram that occurs between 50-100 milliseconds before the actual movement, allowing a real-world application to assist in time. We show that upper limb motion can be estimated from the corresponding muscle activity. To this end, eight-arm muscles are mapped to the joint angle, velocity, and acceleration of the shoulder, elbow, and wrist. For this purpose, we specifically develop an artificial neural network that estimates complex motions involving multiple upper limb joints. The network model is evaluated concerning its ability to generalize across subjects as well as for new motions. This is achieved through training on multiple subjects and additional transfer learning methods so that the prediction for new subjects is significantly improved. In particular, this is beneficial for a robust real-world application. Furthermore, we investigate the importance of the different parameters such as angle, velocity, and acceleration for simple and complex motions. Predictions for simple motions along with the main components of complex motions achieve excellent accuracy while joints that do not play a dominant role during the motion have comparatively lower accuracy.},

keywords = {BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
Motion intention prediction is the key to robot-assisted rehabilitation systems. These can rely on various biological signals. One commonly used signal is the muscle activity measured by an electromyogram that occurs between 50-100 milliseconds before the actual movement, allowing a real-world application to assist in time. We show that upper limb motion can be estimated from the corresponding muscle activity. To this end, eight-arm muscles are mapped to the joint angle, velocity, and acceleration of the shoulder, elbow, and wrist. For this purpose, we specifically develop an artificial neural network that estimates complex motions involving multiple upper limb joints. The network model is evaluated concerning its ability to generalize across subjects as well as for new motions. This is achieved through training on multiple subjects and additional transfer learning methods so that the prediction for new subjects is significantly improved. In particular, this is beneficial for a robust real-world application. Furthermore, we investigate the importance of the different parameters such as angle, velocity, and acceleration for simple and complex motions. Predictions for simple motions along with the main components of complex motions achieve excellent accuracy while joints that do not play a dominant role during the motion have comparatively lower accuracy.
Schließen
2021
61.
 Grün, Felix;  Glasmachers, Tobias;  Iossifidis, Ioannis
Off-Policy Continuous Control Using Distributional Reinforcement Learning Proceedings Article 
In: Bernstein Conference, 2021.
Links | BibTeX | Schlagwörter: Machine Learning, Reinforcement learning
@inproceedings{grunOffPolicyContinuousControl2021b,

title = {Off-Policy Continuous Control Using Distributional Reinforcement Learning},

author = {Felix Grün and Tobias Glasmachers and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2021.p001},

year  = {2021},

date = {2021-10-01},

urldate = {2021-10-01},

publisher = {Bernstein Conference},

keywords = {Machine Learning, Reinforcement learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2021.p001
Schließen
60.
 Lehmler, Stephan Johann;  Saif-ur-Rehman, Muhammad;  Glasmachers, Tobias;  Iossifidis, Ioannis
Transfer-Learning for Patient Specific Model Re-Calibration: Application to sEMG-Classification Proceedings Article 
In: Bernstein Conferen, 2021.
Links | BibTeX | Schlagwörter: BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning, transfer learning
@inproceedings{lehmlerTransferLearningPatientSpecific2021b,

title = {Transfer-Learning for Patient Specific Model Re-Calibration: Application to sEMG-Classification},

author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2021.p005},

year  = {2021},

date = {2021-10-01},

urldate = {2021-10-01},

publisher = {Bernstein Conferen},

keywords = {BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning, transfer learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2021.p005
Schließen
59.
 Schmidt, Marie Dominique;  Glasmachers, Tobias;  Iossifidis, Ioannis
Artificially Generated Muscle Signals Proceedings Article 
In: Bernstein Conference, 2021.
Links | BibTeX | Schlagwörter: BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network
@inproceedings{schmidtArtificiallyGeneratedMuscle2021b,

title = {Artificially Generated Muscle Signals},

author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2021.p111},

year  = {2021},

date = {2021-10-01},

urldate = {2021-10-01},

publisher = {Bernstein Conference},

keywords = {BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2021.p111
Schließen
58.
 Fidencio, Aline Xavier;  Glasmachers, Tobias;  Klaes, Christian;  Iossifidis, Ioannis
Beyond Error Correction: Integration of Error-Related Potentials into Brain-Computer Interfaces for Improved Performance Proceedings Article 
In: Bernstein Conference, 2021.
Links | BibTeX | Schlagwörter: BCI, error-related potentials, Machine Learning, Reinforcement learning
@inproceedings{xavierfidencioErrorCorrectionIntegration2021b,

title = {Beyond Error Correction: Integration of Error-Related Potentials into Brain-Computer Interfaces for Improved Performance},

author = {Aline Xavier Fidencio and Tobias Glasmachers and Christian Klaes and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2021.p163},

year  = {2021},

date = {2021-10-01},

urldate = {2021-10-01},

publisher = {Bernstein Conference},

keywords = {BCI, error-related potentials, Machine Learning, Reinforcement learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2021.p163
Schließen
57.
 Grün, Felix;  Glasmachers, Tobias;  Iossifidis, Ioannis
Off-Policy Continuous Control Using Distributional Reinforcement Learning Proceedings Article 
In: BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021, BCCN Bernstein Network Computational Network, 2021.
Links | BibTeX | Schlagwörter: Machine Learning, Reinforcement learning
@inproceedings{grunOffPolicyContinuousControl2021,

title = {Off-Policy Continuous Control Using Distributional Reinforcement Learning},

author = {Felix Grün and Tobias Glasmachers and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2021.p001},

year  = {2021},

date = {2021-09-15},

urldate = {2021-09-15},

booktitle = {BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021},

publisher = {BCCN Bernstein Network Computational Network},

keywords = {Machine Learning, Reinforcement learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2021.p001
Schließen
56.
 Lehmler, Stephan Johann;  Saif-ur-Rehman, Muhammad;  Glasmachers, Tobias;  Iossifidis, Ioannis
Transfer-Learning for Patient Specific Model Re-Calibration: Application to sEMG-Classification Proceedings Article 
In: BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021, BCCN Bernstein Network Computational Network, 2021.
Links | BibTeX | Schlagwörter: BCI, Machine Learning
@inproceedings{lehmlerTransferLearningPatientSpecific2021,

title = {Transfer-Learning for Patient Specific Model Re-Calibration: Application to sEMG-Classification},

author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2021.p005},

year  = {2021},

date = {2021-09-15},

urldate = {2021-09-15},

booktitle = {BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021},

publisher = {BCCN Bernstein Network Computational Network},

keywords = {BCI, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2021.p005
Schließen
55.
 Schmidt, Marie Dominique;  Glasmachers, Tobias;  Iossifidis, Ioannis
Artificially Generated Muscle Signals Proceedings Article 
In: BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021, BCCN Bernstein Network Computational Network, 2021.
Links | BibTeX | Schlagwörter: BCI, Machine Learning
@inproceedings{schmidtArtificiallyGeneratedMuscle2021,

title = {Artificially Generated Muscle Signals},

author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2021.p111},

year  = {2021},

date = {2021-09-15},

urldate = {2021-09-15},

booktitle = {BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021},

publisher = {BCCN Bernstein Network Computational Network},

keywords = {BCI, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2021.p111
Schließen
54.
 Sziburis, Tim;  Blex, Susanne;  Glasmachers, Tobias;  Rano, Inaki;  Iossifidis, Ioannis
Modelling the Generation of Human Upper-Limb Reaching Trajectories: An Extended Behavioural Attractor Dynamics Approach Proceedings Article 
In: BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021, BCCN Bernstein Network Computational Network, 2021.
Links | BibTeX | Schlagwörter: BCI, Machine Learning, movement model
@inproceedings{sziburisModellingGenerationHuman2021,

title = {Modelling the Generation of Human Upper-Limb Reaching Trajectories: An Extended Behavioural Attractor Dynamics Approach},

author = {Tim Sziburis and Susanne Blex and Tobias Glasmachers and Inaki Rano and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2021.p078},

year  = {2021},

date = {2021-09-15},

urldate = {2021-09-15},

booktitle = {BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021},

publisher = {BCCN Bernstein Network Computational Network},

keywords = {BCI, Machine Learning, movement model},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2021.p078
Schließen
53.
 Ali, Omair;  Saif-ur-Rehman, Muhammad;  Dyck, Susanne;  Glasmachers, Tobias;  Iossifidis, Ioannis;  Klaes, Christian
Anchored-STFT and GNAA: An Extension of STFT in Conjunction with an Adversarial Data Augmentation Technique for the Decoding of Neural Signals Artikel 
In: arXiv:2011.14694 [cs, q-bio], 2021.
Abstract | BibTeX | Schlagwörter: BCI, Machine Learning, Quantitative Biology, Quantitative Methods
@article{aliAnchoredSTFTGNAAExtension2021,

title = {Anchored-STFT and GNAA: An Extension of STFT in Conjunction with an Adversarial Data Augmentation Technique for the Decoding of Neural Signals},

author = {Omair Ali and Muhammad Saif-ur-Rehman and Susanne Dyck and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},

year  = {2021},

date = {2021-08-01},

urldate = {2021-08-01},

journal = {arXiv:2011.14694 [cs, q-bio]},

abstract = {Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one of the most used brain signals in non-invasive BCI applications but are often contaminated with noise. Therefore, it is possible that meaningful patterns for classifying EEG signals are deeply hidden. State-of-the-art deep-learning algorithms are successful in learning hidden, meaningful patterns. However, the quality and the quantity of the presented inputs is pivotal. Here, we propose a novel feature extraction method called anchored Short Time Fourier Transform (anchored-STFT), which is an advanced version of STFT, as it minimizes the trade-off between temporal and spectral resolution presented by STFT. In addition, we propose a novel augmentation method, called gradient norm adversarial augmentation (GNAA). GNAA is not only an augmentation method but is also used to harness adversarial inputs in EEG data, which not only improves the classification accuracy but also enhances the robustness of the classifier. In addition, we also propose a new CNN architecture, namely Skip-Net, for the classification of EEG signals. The proposed pipeline outperforms all state-of-the-art methods and yields an average classification accuracy of 90.7 % and 89.54 % on BCI competition II dataset III and BCI competition IV dataset 2b, respectively.},

keywords = {BCI, Machine Learning, Quantitative Biology, Quantitative Methods},

pubstate = {published},

tppubtype = {article}

}

Schließen
Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one of the most used brain signals in non-invasive BCI applications but are often contaminated with noise. Therefore, it is possible that meaningful patterns for classifying EEG signals are deeply hidden. State-of-the-art deep-learning algorithms are successful in learning hidden, meaningful patterns. However, the quality and the quantity of the presented inputs is pivotal. Here, we propose a novel feature extraction method called anchored Short Time Fourier Transform (anchored-STFT), which is an advanced version of STFT, as it minimizes the trade-off between temporal and spectral resolution presented by STFT. In addition, we propose a novel augmentation method, called gradient norm adversarial augmentation (GNAA). GNAA is not only an augmentation method but is also used to harness adversarial inputs in EEG data, which not only improves the classification accuracy but also enhances the robustness of the classifier. In addition, we also propose a new CNN architecture, namely Skip-Net, for the classification of EEG signals. The proposed pipeline outperforms all state-of-the-art methods and yields an average classification accuracy of 90.7 % and 89.54 % on BCI competition II dataset III and BCI competition IV dataset 2b, respectively.
Schließen
52.
 Fidencio, Aline Xavier;  Glasmachers, Tobias;  Naro, Daniele
Application of Reinforcement Learning to a Mining System Proceedings Article 
In: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), S. 000111–000118, 2021.
Abstract | Links | BibTeX | Schlagwörter: Control Applications, Industrial Application, Machine Learning, Machine learning algorithms, Mining Industry, Reinforcement learning
@inproceedings{fidencioApplicationReinforcementLearning2021,

title = {Application of Reinforcement Learning to a Mining System},

author = {Aline Xavier Fidencio and Tobias Glasmachers and Daniele Naro},

doi = {10.1109/SAMI50585.2021.9378663},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)},

pages = {000111--000118},

abstract = {Automation techniques have been widely applied in different industry segments, among others, to increase both productivity and safety. In the mining industry, with the usage of such systems, the operator can be removed from hazardous environments without compromising task execution and it is possible to achieve more efficient and standardized operation. In this work a study case on the application of machine learning algorithms to a mining system example is presented, in which reinforcement learning algorithms were used to solve a control problem. As an example, a machine chain consisting of a Bucket Wheel Excavator, a Belt Wagon and a Hopper Car was used. This system has two material transfer points that need to remain aligned during operation in order to allow continuous material flow. To keep the alignment, the controller makes use of seven degrees of freedom given by slewing, luffing and crawler drives. Experimental tests were done in a simulated environment with two state-of-the-art algorithms, namely Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). The trained agents were evaluated in terms of episode return and length, as well as alignment quality and action values used. Results show that, for the given task, the PPO agent performs quantitatively and qualitatively better than the SAC agent. However, none of the agents were able to completely solve the proposed testing task.},

keywords = {Control Applications, Industrial Application, Machine Learning, Machine learning algorithms, Mining Industry, Reinforcement learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
Automation techniques have been widely applied in different industry segments, among others, to increase both productivity and safety. In the mining industry, with the usage of such systems, the operator can be removed from hazardous environments without compromising task execution and it is possible to achieve more efficient and standardized operation. In this work a study case on the application of machine learning algorithms to a mining system example is presented, in which reinforcement learning algorithms were used to solve a control problem. As an example, a machine chain consisting of a Bucket Wheel Excavator, a Belt Wagon and a Hopper Car was used. This system has two material transfer points that need to remain aligned during operation in order to allow continuous material flow. To keep the alignment, the controller makes use of seven degrees of freedom given by slewing, luffing and crawler drives. Experimental tests were done in a simulated environment with two state-of-the-art algorithms, namely Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). The trained agents were evaluated in terms of episode return and length, as well as alignment quality and action values used. Results show that, for the given task, the PPO agent performs quantitatively and qualitatively better than the SAC agent. However, none of the agents were able to completely solve the proposed testing task.
Schließen
doi:10.1109/SAMI50585.2021.9378663
Schließen
51.
 Lehmler, Stephan Johann;  Saif-ur-Rehman, Muhammad;  Glasmachers, Tobias;  Iossifidis, Ioannis
Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification Artikel 
In: 2021.
Links | BibTeX | Schlagwörter: BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning
@article{lehmler2021deep,

title = {Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification},

author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},

url = {https://api.semanticscholar.org/CorpusID:245634948},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

keywords = {BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning},

pubstate = {published},

tppubtype = {article}

}

Schließen
https://api.semanticscholar.org/CorpusID:245634948
Schließen
2020
50.
 Hussain, Muhammad Ayaz;  Saif-ur-Rehman, Muhammad;  Klaes, Christian;  Iossifidis, Ioannis
Comparison of Anomaly Detection between Statistical Method and Undercomplete Proceedings Article 
In: IEEE IInternational Congress on Big Data, S. 32–38, Los Angeles, USA, 2020.
Links | BibTeX | Schlagwörter: Anomaly Detection, Autoencoder, Machine Learning
@inproceedings{Hussain2020,

title = {Comparison of Anomaly Detection between Statistical Method and Undercomplete},

author = {Muhammad Ayaz Hussain and Muhammad Saif-ur-Rehman and Christian Klaes and Ioannis Iossifidis},

doi = {https://doi.org/10.1145/3404687.3404689},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

booktitle = {IEEE IInternational Congress on Big Data},

pages = {32--38},

address = {Los Angeles, USA},

keywords = {Anomaly Detection, Autoencoder, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:https://doi.org/10.1145/3404687.3404689
Schließen
49.
 Saif-ur-Rehman, Muhammad;  Ali, Omair;  Dyck, Susanne;  Lienkämper, Robin;  Metzler, Marita;  Parpaley, Yaroslav;  Wellmer, Jörg;  Liu, Charles;  Lee, Brian;  Kellis, Spencer;  Andersen, Richard;  Iossifidis, Ioannis;  Glasmachers, Tobias;  Klaes, Christian
SpikeDeep-Classifier: A deep-learning based fully automatic offline spike sorting algorithm Artikel 
In: Journal of Neural Engineering, 2020.
Abstract | Links | BibTeX | Schlagwörter: BCI, CNN, Machine Learning, Spike Sorting
@article{10.1088/1741-2552/abc8d4,

title = {SpikeDeep-Classifier: A deep-learning based fully automatic offline spike sorting algorithm},

author = {Muhammad Saif-ur-Rehman and Omair Ali and Susanne Dyck and Robin Lienkämper and Marita Metzler and Yaroslav Parpaley and Jörg Wellmer and Charles Liu and Brian Lee and Spencer Kellis and Richard Andersen and Ioannis Iossifidis and Tobias Glasmachers and Christian Klaes},

url = {http://iopscience.iop.org/article/10.1088/1741-2552/abc8d4},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Journal of Neural Engineering},

abstract = {Objective. Advancements in electrode design have resulted in micro-electrode arrays with hundreds of channels for single cell recordings. In the resulting electrophysiological recordings, each implanted electrode can record spike activity (SA) of one or more neurons along with background activity (BA). The aim of this study is to isolate SA of each neural source. This process is called spike sorting or spike classification. Advanced spike sorting algorithms are time consuming because of the human intervention at various stages of the pipeline. Current approaches lack generalization because the values of hyperparameters are not fixed, even for multiple recording sessions of the same subject. In this study, a fully automatic spike sorting algorithm called “SpikeDeep-Classifier” is proposed. The values of hyperparameters remain fixed for all the evaluation data. Approach. The proposed approach is based on our previous study (SpikeDeeptector) and a novel background activity rejector (BAR), which are both supervised learning algorithms and an unsupervised learning algorithm (K-means). SpikeDeeptector and BAR are used to extract meaningful channels and remove BA from the extracted meaningful channels, respectively. The process of clustering becomes straight-forward once the BA is completely removed from the data. Then, K-means with a predefined maximum number of clusters is applied on the remaining data originating from neural sources only. Lastly, a similarity-based criterion and a threshold are used to keep distinct clusters and merge similar looking clusters. The proposed approach is called cluster accept or merge (CAOM) and it has only two hyperparameters (maximum number of clusters and similarity threshold) which are kept fixed for all the evaluation data after tuning. Main Results. We compared the results of our algorithm with ground-truth labels. The algorithm is evaluated on data of human patients and publicly available labeled non-human primates (NHPs) datasets. The average accuracy of BAR on datasets of human patients is 92.3% which is further reduced to 88.03% after (K-means + CAOM). In addition, the average accuracy of BAR on a publicly available labeled dataset of NHPs is 95.40% which reduces to 86.95% after (K-mean + CAOM). Lastly, we compared the performance of the SpikeDeep-Classifier with two human experts, where SpikeDeep-Classifier has produced comparable results. Significance. The results demonstrate that “SpikeDeep-Classifier” possesses the ability to generalize well on a versatile dataset and henceforth provides a generalized well on a versatile dataset and henceforth provides a generalized and fully automated solution to offline spike sorting.},

keywords = {BCI, CNN, Machine Learning, Spike Sorting},

pubstate = {published},

tppubtype = {article}

}

Schließen
Objective. Advancements in electrode design have resulted in micro-electrode arrays with hundreds of channels for single cell recordings. In the resulting electrophysiological recordings, each implanted electrode can record spike activity (SA) of one or more neurons along with background activity (BA). The aim of this study is to isolate SA of each neural source. This process is called spike sorting or spike classification. Advanced spike sorting algorithms are time consuming because of the human intervention at various stages of the pipeline. Current approaches lack generalization because the values of hyperparameters are not fixed, even for multiple recording sessions of the same subject. In this study, a fully automatic spike sorting algorithm called “SpikeDeep-Classifier” is proposed. The values of hyperparameters remain fixed for all the evaluation data. Approach. The proposed approach is based on our previous study (SpikeDeeptector) and a novel background activity rejector (BAR), which are both supervised learning algorithms and an unsupervised learning algorithm (K-means). SpikeDeeptector and BAR are used to extract meaningful channels and remove BA from the extracted meaningful channels, respectively. The process of clustering becomes straight-forward once the BA is completely removed from the data. Then, K-means with a predefined maximum number of clusters is applied on the remaining data originating from neural sources only. Lastly, a similarity-based criterion and a threshold are used to keep distinct clusters and merge similar looking clusters. The proposed approach is called cluster accept or merge (CAOM) and it has only two hyperparameters (maximum number of clusters and similarity threshold) which are kept fixed for all the evaluation data after tuning. Main Results. We compared the results of our algorithm with ground-truth labels. The algorithm is evaluated on data of human patients and publicly available labeled non-human primates (NHPs) datasets. The average accuracy of BAR on datasets of human patients is 92.3% which is further reduced to 88.03% after (K-means + CAOM). In addition, the average accuracy of BAR on a publicly available labeled dataset of NHPs is 95.40% which reduces to 86.95% after (K-mean + CAOM). Lastly, we compared the performance of the SpikeDeep-Classifier with two human experts, where SpikeDeep-Classifier has produced comparable results. Significance. The results demonstrate that “SpikeDeep-Classifier” possesses the ability to generalize well on a versatile dataset and henceforth provides a generalized well on a versatile dataset and henceforth provides a generalized and fully automated solution to offline spike sorting.
Schließen
http://iopscience.iop.org/article/10.1088/1741-2552/abc8d4
Schließen
48.
 Ali, Omair;  Saif-ur-Rehman, Muhammad;  Dyck, Susanne;  Glasmachers, Tobias;  Iossifidis, Ioannis;  Klaes, Christian
Improving the performance of EEG decoding using anchored-STFT in conjunction with gradient norm adversarial augmentation Artikel 
In: arXiv preprint arXiv:2011.14694, 2020.
BibTeX | Schlagwörter: Adversarial NN, BCI, EEG, Machine Learning
@article{ali2020improving,

title = {Improving the performance of EEG decoding using anchored-STFT in conjunction with gradient norm adversarial augmentation},

author = {Omair Ali and Muhammad Saif-ur-Rehman and Susanne Dyck and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {arXiv preprint arXiv:2011.14694},

keywords = {Adversarial NN, BCI, EEG, Machine Learning},

pubstate = {published},

tppubtype = {article}

}

Schließen
2019
47.
 Saif-ur-Rehman, Muhammad;  Lienkämper, Robin;  Dyck, Susanne;  Rayana, A;  Parpaley, Y;  Wllner, J;  Liu, Charles;  Lee, Brian;  Kellis, Spencer;  Manahan-Vaughn, D;  Güntürkün, O;  Andersen, Richard;  Iossifidis, Ioannis;  Glasmachers, Tobias;  Klaes, Christian
Universal SpikeDeeptector Sonstige 
2019.
Abstract | BibTeX | Schlagwörter: BCI, CNN, Machine Learning, Spike Detection, Spike Sorting
@misc{ur-reimann2019a,

title = {Universal SpikeDeeptector},

author = {Muhammad Saif-ur-Rehman and Robin Lienkämper and Susanne Dyck and A Rayana and Y Parpaley and J Wllner and Charles Liu and Brian Lee and Spencer Kellis and D Manahan-Vaughn and O Güntürkün and Richard Andersen and Ioannis Iossifidis and Tobias Glasmachers and Christian Klaes},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

publisher = {SfN 2019},

abstract = {State-of-the-art microelectrode array technology enables simultaneous, large-scale single unit recordings from hundreds of channels. Identification of channels recording neural data as compared to noise is the first step for all further analyses. Automatizing this process aims at minimizing the human involvement and time for manual curation. In our previous study, we introduced the “SpikeDeeptector” (SD), which enables us to automatically detect and track channels containing neural data from different human patients implanted with different types of microelectrodes across different brain areas. SD works on human data and to some extent on the data of non-human primates (NHPs). However, to make SD more versatile we proposed a more generalized method called “Universal SpikeDeeptector (USD)”, which is an extended version of SD. USD intends to detect and track the channels containing neural data recorded from four different species (rats, ravens, NHPs and humans) using different kinds of microelectrodes and different recording sites. To our knowledge, there is no method that can simultaneously detect and track neural data of multiple species. To enable contextual learning, USD constructs a feature vector from a batch of waveforms. The constructed feature vectors are then fed into a deep-learning algorithm, which learns contextualized, temporal and spatial patterns. USD is a supervised learning method. Therefore, it requires labeled data for training. It is mainly trained on data from a single human tetraplegic patient, and a small but equal portion of data from the remaining three species. The trained model is then evaluated on a test dataset collected from several humans, NHPs, rats, and birds. The results show that the USD performed consistently well across data collected from each species.},

keywords = {BCI, CNN, Machine Learning, Spike Detection, Spike Sorting},

pubstate = {published},

tppubtype = {misc}

}

Schließen
State-of-the-art microelectrode array technology enables simultaneous, large-scale single unit recordings from hundreds of channels. Identification of channels recording neural data as compared to noise is the first step for all further analyses. Automatizing this process aims at minimizing the human involvement and time for manual curation. In our previous study, we introduced the “SpikeDeeptector” (SD), which enables us to automatically detect and track channels containing neural data from different human patients implanted with different types of microelectrodes across different brain areas. SD works on human data and to some extent on the data of non-human primates (NHPs). However, to make SD more versatile we proposed a more generalized method called “Universal SpikeDeeptector (USD)”, which is an extended version of SD. USD intends to detect and track the channels containing neural data recorded from four different species (rats, ravens, NHPs and humans) using different kinds of microelectrodes and different recording sites. To our knowledge, there is no method that can simultaneously detect and track neural data of multiple species. To enable contextual learning, USD constructs a feature vector from a batch of waveforms. The constructed feature vectors are then fed into a deep-learning algorithm, which learns contextualized, temporal and spatial patterns. USD is a supervised learning method. Therefore, it requires labeled data for training. It is mainly trained on data from a single human tetraplegic patient, and a small but equal portion of data from the remaining three species. The trained model is then evaluated on a test dataset collected from several humans, NHPs, rats, and birds. The results show that the USD performed consistently well across data collected from each species.
Schließen
46.
 Hussain, Muhammad Ayaz;  Saif-ur-Rehman, Muhammad;  Klaes, Christian;  Iossifidis, Ioannis
Comparison of Anomaly Detection between Statistical Method and Undercomplete Proceedings Article 
In: IEEE IInternational Congress on Big Data, Los Angeles, USA, 2019.
BibTeX | Schlagwörter: Anomaly Detection, Autoencoder, Machine Learning
@inproceedings{Hussain2019,

title = {Comparison of Anomaly Detection between Statistical Method and Undercomplete},

author = {Muhammad Ayaz Hussain and Muhammad Saif-ur-Rehman and Christian Klaes and Ioannis Iossifidis},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

booktitle = {IEEE IInternational Congress on Big Data},

address = {Los Angeles, USA},

keywords = {Anomaly Detection, Autoencoder, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
45.
 Saif-ur-Rehman, Muhammad;  Lienkämper, Robin;  Parpaley, Yaroslav;  Wellmer, Jörg;  Liu, Charles;  Lee, Brian;  Kellis, Spencer;  Andersen, Richard;  Iossifidis, Ioannis;  Glasmachers, Tobias;  Klaes, Christian
SpikeDeeptector: a deep-learning based method for detection of neural spiking activity Artikel 
In: Journal of Neural Engineering, Bd. 16, Nr. 5, S. 056003, 2019.
Abstract | Links | BibTeX | Schlagwörter: BCI, CNN, Data Reduction, Machine Learning, Spike Sorting
@article{Saif-ur-Rehman2019,

title = {SpikeDeeptector: a deep-learning based method for detection of neural spiking activity},

author = {Muhammad Saif-ur-Rehman and Robin Lienkämper and Yaroslav Parpaley and Jörg Wellmer and Charles Liu and Brian Lee and Spencer Kellis and Richard Andersen and Ioannis Iossifidis and Tobias Glasmachers and Christian Klaes},

url = {https://iopscience.iop.org/article/10.1088/1741-2552/ab1e63/meta},

doi = {10.1088/1741-2552/ab1e63},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Journal of Neural Engineering},

volume = {16},

number = {5},

pages = {056003},

abstract = {Objective . In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed. For further analysis, an automatic identification and continuous tracking of channels containing neural data is of great significance for many applications, e.g. automated selection of neural channels during online and offline spike sorting. Automated spike detection and sorting is also critical for online decoding in brain–computer interface (BCI) applications, in which on...},

keywords = {BCI, CNN, Data Reduction, Machine Learning, Spike Sorting},

pubstate = {published},

tppubtype = {article}

}

Schließen
Objective . In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed. For further analysis, an automatic identification and continuous tracking of channels containing neural data is of great significance for many applications, e.g. automated selection of neural channels during online and offline spike sorting. Automated spike detection and sorting is also critical for online decoding in brain–computer interface (BCI) applications, in which on...
Schließen
https://iopscience.iop.org/article/10.1088/1741-2552/ab1e63/meta
doi:10.1088/1741-2552/ab1e63
Schließen
2012
44.
 Noth, S;  Edelbrunner, Johann;  Iossifidis, Ioannis
An integrated architecture for the development and assessment of ADAS Proceedings Article 
In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2012, ISBN: 9781467330640.
Abstract | Links | BibTeX | Schlagwörter: ADAS, Autonomous Driving, Machine Learning
@inproceedings{Noth2012a,

title = {An integrated architecture for the development and assessment of ADAS},

author = {S Noth and Johann Edelbrunner and Ioannis Iossifidis},

doi = {10.1109/ITSC.2012.6338805},

isbn = {9781467330640},

year  = {2012},

date = {2012-01-01},

urldate = {2012-01-01},

booktitle = {IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC},

abstract = {Advanced Driver Assistant Systems act, by definition in natural, often poorly structured, environments and are supposed to closely interact with human operators. Both, natural environments as well as human behaviour have no inherent metric and can not be modelled/measured in the classical way physically plausibly behaving systems are described. textcopyright 2012 IEEE.},

keywords = {ADAS, Autonomous Driving, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
Advanced Driver Assistant Systems act, by definition in natural, often poorly structured, environments and are supposed to closely interact with human operators. Both, natural environments as well as human behaviour have no inherent metric and can not be modelled/measured in the classical way physically plausibly behaving systems are described. textcopyright 2012 IEEE.
Schließen
doi:10.1109/ITSC.2012.6338805
Schließen
43.
 Noth, Sebastian;  Edelbrunner, Johann;  Iossifidis, Ioannis
A Versatile Simulated Reality Framework: From Embedded Components to ADAS Proceedings Article 
In: International Conference on Pervasive and Embedded and Communication Systems, 2012, PECCS2012, 2012.
BibTeX | Schlagwörter: Autonomous robotics, Machine Learning, simulated reality, Simulation, virtual reality
@inproceedings{Noth2012b,

title = {A Versatile Simulated Reality Framework: From Embedded Components to ADAS},

author = {Sebastian Noth and Johann Edelbrunner and Ioannis Iossifidis},

year  = {2012},

date = {2012-01-01},

booktitle = {International Conference on Pervasive and Embedded and Communication Systems, 2012, PECCS2012},

keywords = {Autonomous robotics, Machine Learning, simulated reality, Simulation, virtual reality},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
2011
42.
 Dogan, Ürün;  Edelbrunner, Johann;  Iossifidis, Ioannis
Autonomous driving: A comparison of machine learning techniques by means of the prediction of lane change behavior Proceedings Article 
In: 2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011, S. 1837–1843, 2011, ISSN: 01962892.
Abstract | Links | BibTeX | Schlagwörter: ADAS, Autonomous Driving, lane change prediction, Machine Learning
@inproceedings{Dogan2011a,

title = {Autonomous driving: A comparison of machine learning techniques by means of the prediction of lane change behavior},

author = {Ürün Dogan and Johann Edelbrunner and Ioannis Iossifidis},

doi = {10.1109/ROBIO.2011.6181557},

issn = {01962892},

year  = {2011},

date = {2011-01-01},

booktitle = {2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011},

pages = {1837--1843},

abstract = {In the presented work we compare machine learning techniques in the context of lane change behavior performed by humans in a semi-naturalistic simulated environment. We evaluate different learning approaches using differing feature combinations in order to identify appropriate feature, best feature combination, and the most appropriate machine learning technique for the described task. Based on the data acquired from human drivers in the traffic simulator NISYS TRS1, we trained a recurrent neural network, a feed forward neural network and a set of support vector machines. In the followed test drives the system was able to predict lane changes up to 1.5 sec in beforehand.},

keywords = {ADAS, Autonomous Driving, lane change prediction, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
In the presented work we compare machine learning techniques in the context of lane change behavior performed by humans in a semi-naturalistic simulated environment. We evaluate different learning approaches using differing feature combinations in order to identify appropriate feature, best feature combination, and the most appropriate machine learning technique for the described task. Based on the data acquired from human drivers in the traffic simulator NISYS TRS1, we trained a recurrent neural network, a feed forward neural network and a set of support vector machines. In the followed test drives the system was able to predict lane changes up to 1.5 sec in beforehand.
Schließen
doi:10.1109/ROBIO.2011.6181557
Schließen
41.
 Dogan, Urun;  Edelbrunner, Johann;  Iossifidis, Ioannis
Autonomous Driving: A Comparison of Machine Learning Techniques by Measns of the Prediction of Lane Change Behavior Proceedings Article 
In: Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2011), 2011.
BibTeX | Schlagwörter: driver information systems, feed forward neural network, lane change maneuvers, Machine Learning, recurrent neural network, support vector machines, traffic simulator
@inproceedings{Dogan2011b,

title = {Autonomous Driving: A Comparison of Machine Learning Techniques by Measns of the Prediction of Lane Change Behavior},

author = {Urun Dogan and Johann Edelbrunner and Ioannis Iossifidis},

year  = {2011},

date = {2011-01-01},

urldate = {2011-01-01},

booktitle = {Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2011)},

keywords = {driver information systems, feed forward neural network, lane change maneuvers, Machine Learning, recurrent neural network, support vector machines, traffic simulator},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
40.
 Noth, Sebastian;  Iossifidis, Ioannis
Simulated reality environment for development and assessment of cognitive robotic systems Proceedings Article 
In: Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2011), 2011.
Abstract | BibTeX | Schlagwörter: Autonomous robotics, Machine Learning, Simulation, virtual reality
@inproceedings{Noth2011,

title = {Simulated reality environment for development and assessment of cognitive robotic systems},

author = {Sebastian Noth and Ioannis Iossifidis},

year  = {2011},

date = {2011-01-01},

urldate = {2011-01-01},

booktitle = {Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2011)},

abstract = {Simulated reality environment incorporating humans and physically plausible behaving robots, providing natural interaction channels, with the option to link simulator to real perception and motion, is gaining importance for the development of cognitive, intuitive interacting and collaborating robotic systems. 

 

 In the present work we introduce a head tracking system which is utilized to incorporate human ego motion in simulated environment improving immersion in the context of human-robot collaborative tasks.},

keywords = {Autonomous robotics, Machine Learning, Simulation, virtual reality},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
Simulated reality environment incorporating humans and physically plausible behaving robots, providing natural interaction channels, with the option to link simulator to real perception and motion, is gaining importance for the development of cognitive, intuitive interacting and collaborating robotic systems. 

 

 In the present work we introduce a head tracking system which is utilized to incorporate human ego motion in simulated environment improving immersion in the context of human-robot collaborative tasks.
Schließen
39.
 Noth, S;  Iossifidis, Ioannis
Benefits of ego motion feedback for interactive experiments in virtual reality scenarios Konferenz 
BC11 : Computational Neuroscience $backslash$& Neurotechnology Bernstein Conference $backslash$& Neurex Annual Meeting 2011, 2011.
BibTeX | Schlagwörter: Autonomous robotics, Machine Learning, simulated reality, Simulation, virtual reality
@conference{Noth2011a,

title = {Benefits of ego motion feedback for interactive experiments in virtual reality scenarios},

author = {S Noth and Ioannis Iossifidis},

year  = {2011},

date = {2011-01-01},

urldate = {2011-01-01},

booktitle = {BC11 : Computational Neuroscience $backslash$& Neurotechnology Bernstein Conference $backslash$& Neurex Annual Meeting 2011},

keywords = {Autonomous robotics, Machine Learning, simulated reality, Simulation, virtual reality},

pubstate = {published},

tppubtype = {conference}

}

Schließen
2008
38.
 Dogan, Ueruen;  Edelbrunner, Hannes;  Iossifidis, Ioannis
Towards a Driver Model: Preliminary Study of Lane Change Behavior Proceedings Article 
In: 2008 11th International IEEE Conference on Intelligent Transportation Systems, S. 931–937, IEEE, 2008, ISBN: 978-1-4244-2111-4.
Abstract | Links | BibTeX | Schlagwörter: driver information systems, driver model, drivers lane change behavior prediction, feed forward neural network, feedforward neural nets, lane change maneuvers, Machine Learning, recurrent neural nets, recurrent neural network, support vector machines, traffic simulator
@inproceedings{Dogan2008b,

title = {Towards a Driver Model: Preliminary Study of Lane Change Behavior},

author = {Ueruen Dogan and Hannes Edelbrunner and Ioannis Iossifidis},

url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4732700},

doi = {10.1109/ITSC.2008.4732700},

isbn = {978-1-4244-2111-4},

year  = {2008},

date = {2008-10-01},

booktitle = {2008 11th International IEEE Conference on Intelligent Transportation Systems},

pages = {931--937},

publisher = {IEEE},

abstract = {The presented work formulates an framework in which early prediction of drivers lane change behavior is realized. We aim to build a representation of drivers lane change behavior in order to recognize and to predict driver's intentions as a first step towards a realistic driver model. In the test bed of the Institute of Neuroinformatik, based on the traffic simulator NISYS TRS 1, 10 individuals have driven in the experiments and they performed more then 150 lane change maneuvers. Lane-offset, distance to the front car and time to contact, were recorded. The acquired data was used to train - in parallel- a recurrent neural network, a feed forward neural network and a set of support vector machines. In the followed test drives the system was able of performing a lane change prediction time of 1.5 sec beforehand. The proposed approach describes a framework for lane-change detection and prediction, which will serve as a prerequisite for a successful driver model.},

keywords = {driver information systems, driver model, drivers lane change behavior prediction, feed forward neural network, feedforward neural nets, lane change maneuvers, Machine Learning, recurrent neural nets, recurrent neural network, support vector machines, traffic simulator},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
The presented work formulates an framework in which early prediction of drivers lane change behavior is realized. We aim to build a representation of drivers lane change behavior in order to recognize and to predict driver's intentions as a first step towards a realistic driver model. In the test bed of the Institute of Neuroinformatik, based on the traffic simulator NISYS TRS 1, 10 individuals have driven in the experiments and they performed more then 150 lane change maneuvers. Lane-offset, distance to the front car and time to contact, were recorded. The acquired data was used to train - in parallel- a recurrent neural network, a feed forward neural network and a set of support vector machines. In the followed test drives the system was able of performing a lane change prediction time of 1.5 sec beforehand. The proposed approach describes a framework for lane-change detection and prediction, which will serve as a prerequisite for a successful driver model.
Schließen
http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4732700
doi:10.1109/ITSC.2008.4732700
Schließen
2003
37.
 Iossifidis, Ioannis;  Theis, Christoph;  Grote, Claudia;  Faubel, Christian;  Schoner, Gregor
Anthropomorphism as a pervasive design concept for a robotic assistant Proceedings Article 
In: Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), S. 3465––3472 vol.3, 2003.
Abstract | Links | BibTeX | Schlagwörter: anthropomorphic robot arm, anthropomorphism, force sensing, grippers, human anatomy, human motor behavior, human operators intuition, human perception, Machine Learning, man-machine systems, manipulators, mechanical structure modeling, object recognition, operators gaze, operators gesture, pervasive design, robot vision, robotic assistant, robots gripper, sensory channels, user modelling
@inproceedings{Iossifidis2003e,

title = {Anthropomorphism as a pervasive design concept for a robotic assistant},

author = {Ioannis Iossifidis and Christoph Theis and Claudia Grote and Christian Faubel and Gregor Schoner},

doi = {10.1109/IROS.2003.1249692},

year  = {2003},

date = {2003-01-01},

booktitle = {Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003)},

volume = {4},

pages = {3465----3472 vol.3},

abstract = {CORA is a robotic assistant whose task is to collaborate with a human operator on simple manipulation or handling tasks. Its sensory channels comprising vision, audition, haptics, and force sensing are used to extract perceptual information about speech, gestures and gaze of the operator, and object recognition. The anthropomorphic robot arm makes goal-directed movements to pick up and hand over objects. The human operator may mechanically interact with the arm by pushing it away (haptics) or by taking an object out of the robot's gripper (force sensing). The design objective has been to exploit the human operator's intuition by modeling the mechanical structure, the senses, and the behaviors of the assistant on human anatomy, human perception, and human motor behavior.},

keywords = {anthropomorphic robot arm, anthropomorphism, force sensing, grippers, human anatomy, human motor behavior, human operators intuition, human perception, Machine Learning, man-machine systems, manipulators, mechanical structure modeling, object recognition, operators gaze, operators gesture, pervasive design, robot vision, robotic assistant, robots gripper, sensory channels, user modelling},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
CORA is a robotic assistant whose task is to collaborate with a human operator on simple manipulation or handling tasks. Its sensory channels comprising vision, audition, haptics, and force sensing are used to extract perceptual information about speech, gestures and gaze of the operator, and object recognition. The anthropomorphic robot arm makes goal-directed movements to pick up and hand over objects. The human operator may mechanically interact with the arm by pushing it away (haptics) or by taking an object out of the robot's gripper (force sensing). The design objective has been to exploit the human operator's intuition by modeling the mechanical structure, the senses, and the behaviors of the assistant on human anatomy, human perception, and human motor behavior.
Schließen
doi:10.1109/IROS.2003.1249692
Schließen
36.
 Iossifidis, Ioannis;  Theis, C;  Grote, C;  Faubel, Christian;  Schöner, G
Anthropomorphism as a pervasive design concept for a robotic assistant Proceedings Article 
In: Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453), S. 3465–3472, IEEE, 2003, ISBN: 0-7803-7860-1.
Abstract | Links | BibTeX | Schlagwörter: anthropomorphic robot arm, anthropomorphism, force sensing, grippers, human anatomy, human motor behavior, human operators intuition, human perception, Machine Learning, man-machine systems, manipulators, mechanical structure modeling, object recognition, operators gaze, operators gesture, pervasive design, robot vision, robotic assistant, robots gripper, sensory channels, user modelling
@inproceedings{Iossifidis2003bc,

title = {Anthropomorphism as a pervasive design concept for a robotic assistant},

author = {Ioannis Iossifidis and C Theis and C Grote and Christian Faubel and G Schöner},

doi = {10.1109/IROS.2003.1249692},

isbn = {0-7803-7860-1},

year  = {2003},

date = {2003-01-01},

urldate = {2003-01-01},

booktitle = {Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453)},

volume = {4},

pages = {3465--3472},

publisher = {IEEE},

abstract = {CORA is a robotic assistant whose task is to collaborate with a human operator on simple manipulation or handling tasks. Its sensory channels comprising vision, audition, haptics, and force sensing are used to extract perceptual information about speech, gestures and gaze of the operator, and object recognition. The anthropomorphic robot arm makes goal-directed movements to pick up and hand over objects. The human operator may mechanically interact with the arm by pushing it away (haptics) or by taking an object out of the robot's gripper (force sensing). The design objective has been to exploit the human operator's intuition by modeling the mechanical structure, the senses, and the behaviors of the assistant on human anatomy, human perception, and human motor behavior.},

keywords = {anthropomorphic robot arm, anthropomorphism, force sensing, grippers, human anatomy, human motor behavior, human operators intuition, human perception, Machine Learning, man-machine systems, manipulators, mechanical structure modeling, object recognition, operators gaze, operators gesture, pervasive design, robot vision, robotic assistant, robots gripper, sensory channels, user modelling},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
CORA is a robotic assistant whose task is to collaborate with a human operator on simple manipulation or handling tasks. Its sensory channels comprising vision, audition, haptics, and force sensing are used to extract perceptual information about speech, gestures and gaze of the operator, and object recognition. The anthropomorphic robot arm makes goal-directed movements to pick up and hand over objects. The human operator may mechanically interact with the arm by pushing it away (haptics) or by taking an object out of the robot's gripper (force sensing). The design objective has been to exploit the human operator's intuition by modeling the mechanical structure, the senses, and the behaviors of the assistant on human anatomy, human perception, and human motor behavior.
Schließen
doi:10.1109/IROS.2003.1249692
Schließen
35.
 Iossifidis, Ioannis;  Theis, Christoph;  Grote, Claudia;  Faubel, Christian;  Schoner, Gregor
Anthropomorphism as a pervasive design concept for a robotic assistant Proceedings Article 
In: Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), S. 3465––3472 vol.3, 2003.
Abstract | Links | BibTeX | Schlagwörter: anthropomorphic robot arm, anthropomorphism, force sensing, grippers, human anatomy, human motor behavior, human operators intuition, human perception, Machine Learning, man-machine systems, manipulators, mechanical structure modeling, object recognition, operators gaze, operators gesture, pervasive design, robot vision, robotic assistant, robots gripper, sensory channels, user modelling
@inproceedings{Iossifidis2003,

title = {Anthropomorphism as a pervasive design concept for a robotic assistant},

author = {Ioannis Iossifidis and Christoph Theis and Claudia Grote and Christian Faubel and Gregor Schoner},

doi = {10.1109/IROS.2003.1249692},

year  = {2003},

date = {2003-01-01},

booktitle = {Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003)},

volume = {4},

pages = {3465----3472 vol.3},

abstract = {CORA is a robotic assistant whose task is to collaborate with a human 

operator on simple manipulation or handling tasks. Its sensory channels 

comprising vision, audition, haptics, and force sensing are used 

to extract perceptual information about speech, gestures and gaze 

of the operator, and object recognition. The anthropomorphic robot 

arm makes goal-directed movements to pick up and hand over objects. 

The human operator may mechanically interact with the arm by pushing 

it away (haptics) or by taking an object out of the robot's gripper 

(force sensing). The design objective has been to exploit the human 

operator's intuition by modeling the mechanical structure, the senses, 

and the behaviors of the assistant on human anatomy, human perception, 

and human motor behavior.},

keywords = {anthropomorphic robot arm, anthropomorphism, force sensing, grippers, human anatomy, human motor behavior, human operators intuition, human perception, Machine Learning, man-machine systems, manipulators, mechanical structure modeling, object recognition, operators gaze, operators gesture, pervasive design, robot vision, robotic assistant, robots gripper, sensory channels, user modelling},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
CORA is a robotic assistant whose task is to collaborate with a human 

operator on simple manipulation or handling tasks. Its sensory channels 

comprising vision, audition, haptics, and force sensing are used 

to extract perceptual information about speech, gestures and gaze 

of the operator, and object recognition. The anthropomorphic robot 

arm makes goal-directed movements to pick up and hand over objects. 

The human operator may mechanically interact with the arm by pushing 

it away (haptics) or by taking an object out of the robot's gripper 

(force sensing). The design objective has been to exploit the human 

operator's intuition by modeling the mechanical structure, the senses, 

and the behaviors of the assistant on human anatomy, human perception, 

and human motor behavior.
Schließen
doi:10.1109/IROS.2003.1249692
Schließen
34.
 Iossifidis, Ioannis;  Theis, C;  Grote, C;  Faubel, Christian;  Schöner, G
Anthropomorphism as a pervasive design concept for a robotic assistant Proceedings Article 
In: Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453), S. 3465–3472, IEEE, 2003, ISBN: 0-7803-7860-1.
Abstract | Links | BibTeX | Schlagwörter: Machine Learning
@inproceedings{Iossifidis2003b,

title = {Anthropomorphism as a pervasive design concept for a robotic assistant},

author = {Ioannis Iossifidis and C Theis and C Grote and Christian Faubel and G Schöner},

doi = {10.1109/IROS.2003.1249692},

isbn = {0-7803-7860-1},

year  = {2003},

date = {2003-01-01},

urldate = {2003-01-01},

booktitle = {Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453)},

volume = {4},

pages = {3465--3472},

publisher = {IEEE},

abstract = {CORA is a robotic assistant whose task is to collaborate with a human operator on simple manipulation or handling tasks. Its sensory channels comprising vision, audition, haptics, and force sensing are used to extract perceptual information about speech, gestures and gaze of the operator, and object recognition. The anthropomorphic robot arm makes goal-directed movements to pick up and hand over objects. The human operator may mechanically interact with the arm by pushing it away (haptics) or by taking an object out of the robot's gripper (force sensing). The design objective has been to exploit the human operator's intuition by modeling the mechanical structure, the senses, and the behaviors of the assistant on human anatomy, human perception, and human motor behavior.},

keywords = {Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
CORA is a robotic assistant whose task is to collaborate with a human operator on simple manipulation or handling tasks. Its sensory channels comprising vision, audition, haptics, and force sensing are used to extract perceptual information about speech, gestures and gaze of the operator, and object recognition. The anthropomorphic robot arm makes goal-directed movements to pick up and hand over objects. The human operator may mechanically interact with the arm by pushing it away (haptics) or by taking an object out of the robot's gripper (force sensing). The design objective has been to exploit the human operator's intuition by modeling the mechanical structure, the senses, and the behaviors of the assistant on human anatomy, human perception, and human motor behavior.
Schließen
doi:10.1109/IROS.2003.1249692
Schließen
33.
 Iossifidis, Ioannis;  Theis, Christoph;  Grote, Claudia;  Faubel, Christian;  Schoner, Gregor
Anthropomorphism as a Pervasive Design Concept for a Robotic Assistant Proceedings Article 
In: Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), S. 3465––3472 vol.3, 2003.
Abstract | Links | BibTeX | Schlagwörter: anthropomorphic robot arm, anthropomorphism, force sensing, grippers, human anatomy, human motor behavior, human operators intuition, human perception, Machine Learning, man-machine systems, manipulators, mechanical structure modeling, object recognition, operators gaze, operators gesture, pervasive design, robot vision, robotic assistant, robots gripper, sensory channels, user modelling
@inproceedings{iossifidisAnthropomorphismPervasiveDesign2003c,

title = {Anthropomorphism as a Pervasive Design Concept for a Robotic Assistant},

author = {Ioannis Iossifidis and Christoph Theis and Claudia Grote and Christian Faubel and Gregor Schoner},

doi = {10.1109/IROS.2003.1249692},

year  = {2003},

date = {2003-01-01},

booktitle = {Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003)},

volume = {4},

pages = {3465----3472 vol.3},

abstract = {CORA is a robotic assistant whose task is to collaborate with a human operator on simple manipulation or handling tasks. Its sensory channels comprising vision, audition, haptics, and force sensing are used to extract perceptual information about speech, gestures and gaze of the operator, and object recognition. The anthropomorphic robot arm makes goal-directed movements to pick up and hand over objects. The human operator may mechanically interact with the arm by pushing it away (haptics) or by taking an object out of the robot's gripper (force sensing). The design objective has been to exploit the human operator's intuition by modeling the mechanical structure, the senses, and the behaviors of the assistant on human anatomy, human perception, and human motor behavior.},

keywords = {anthropomorphic robot arm, anthropomorphism, force sensing, grippers, human anatomy, human motor behavior, human operators intuition, human perception, Machine Learning, man-machine systems, manipulators, mechanical structure modeling, object recognition, operators gaze, operators gesture, pervasive design, robot vision, robotic assistant, robots gripper, sensory channels, user modelling},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
CORA is a robotic assistant whose task is to collaborate with a human operator on simple manipulation or handling tasks. Its sensory channels comprising vision, audition, haptics, and force sensing are used to extract perceptual information about speech, gestures and gaze of the operator, and object recognition. The anthropomorphic robot arm makes goal-directed movements to pick up and hand over objects. The human operator may mechanically interact with the arm by pushing it away (haptics) or by taking an object out of the robot's gripper (force sensing). The design objective has been to exploit the human operator's intuition by modeling the mechanical structure, the senses, and the behaviors of the assistant on human anatomy, human perception, and human motor behavior.
Schließen
doi:10.1109/IROS.2003.1249692
Schließen
32.
 Iossifidis, Ioannis;  Theis, C.;  Grote, C.;  Faubel, Christian;  Schöner, G.
Anthropomorphism as a Pervasive Design Concept for a Robotic Assistant Proceedings Article 
In: Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453), S. 3465–3472, IEEE, 2003, ISBN: 0-7803-7860-1.
Abstract | Links | BibTeX | Schlagwörter: Machine Learning
@inproceedings{iossifidisAnthropomorphismPervasiveDesign2003ab,

title = {Anthropomorphism as a Pervasive Design Concept for a Robotic Assistant},

author = {Ioannis Iossifidis and C. Theis and C. Grote and Christian Faubel and G. Schöner},

doi = {10.1109/IROS.2003.1249692},

isbn = {0-7803-7860-1},

year  = {2003},

date = {2003-01-01},

urldate = {2003-01-01},

booktitle = {Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453)},

volume = {4},

pages = {3465--3472},

publisher = {IEEE},

abstract = {CORA is a robotic assistant whose task is to collaborate with a human operator on simple manipulation or handling tasks. Its sensory channels comprising vision, audition, haptics, and force sensing are used to extract perceptual information about speech, gestures and gaze of the operator, and object recognition. The anthropomorphic robot arm makes goal-directed movements to pick up and hand over objects. The human operator may mechanically interact with the arm by pushing it away (haptics) or by taking an object out of the robot's gripper (force sensing). The design objective has been to exploit the human operator's intuition by modeling the mechanical structure, the senses, and the behaviors of the assistant on human anatomy, human perception, and human motor behavior.},

keywords = {Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
CORA is a robotic assistant whose task is to collaborate with a human operator on simple manipulation or handling tasks. Its sensory channels comprising vision, audition, haptics, and force sensing are used to extract perceptual information about speech, gestures and gaze of the operator, and object recognition. The anthropomorphic robot arm makes goal-directed movements to pick up and hand over objects. The human operator may mechanically interact with the arm by pushing it away (haptics) or by taking an object out of the robot's gripper (force sensing). The design objective has been to exploit the human operator's intuition by modeling the mechanical structure, the senses, and the behaviors of the assistant on human anatomy, human perception, and human motor behavior.
Schließen
doi:10.1109/IROS.2003.1249692
Schließen
31.
 Iossifidis, Ioannis;  Theis, Christoph;  Grote, Claudia;  Faubel, Christian;  Schoner, Gregor
Anthropomorphism as a pervasive design concept for a robotic assistant Proceedings Article 
In: Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), S. 3465––3472 vol.3, 2003.
Abstract | Links | BibTeX | Schlagwörter: anthropomorphic robot arm, anthropomorphism, force sensing, grippers, human anatomy, human motor behavior, human operators intuition, human perception, Machine Learning, man-machine systems, manipulators, mechanical structure modeling, object recognition, operators gaze, operators gesture, pervasive design, robot vision, robotic assistant, robots gripper, sensory channels, user modelling
@inproceedings{Iossifidis2003c,

title = {Anthropomorphism as a pervasive design concept for a robotic assistant},

author = {Ioannis Iossifidis and Christoph Theis and Claudia Grote and Christian Faubel and Gregor Schoner},

doi = {10.1109/IROS.2003.1249692},

year  = {2003},

date = {2003-01-01},

booktitle = {Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003)},

volume = {4},

pages = {3465----3472 vol.3},

abstract = {CORA is a robotic assistant whose task is to collaborate with a human operator on simple manipulation or handling tasks. Its sensory channels comprising vision, audition, haptics, and force sensing are used to extract perceptual information about speech, gestures and gaze of the operator, and object recognition. The anthropomorphic robot arm makes goal-directed movements to pick up and hand over objects. The human operator may mechanically interact with the arm by pushing it away (haptics) or by taking an object out of the robot's gripper (force sensing). The design objective has been to exploit the human operator's intuition by modeling the mechanical structure, the senses, and the behaviors of the assistant on human anatomy, human perception, and human motor behavior.},

keywords = {anthropomorphic robot arm, anthropomorphism, force sensing, grippers, human anatomy, human motor behavior, human operators intuition, human perception, Machine Learning, man-machine systems, manipulators, mechanical structure modeling, object recognition, operators gaze, operators gesture, pervasive design, robot vision, robotic assistant, robots gripper, sensory channels, user modelling},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
CORA is a robotic assistant whose task is to collaborate with a human operator on simple manipulation or handling tasks. Its sensory channels comprising vision, audition, haptics, and force sensing are used to extract perceptual information about speech, gestures and gaze of the operator, and object recognition. The anthropomorphic robot arm makes goal-directed movements to pick up and hand over objects. The human operator may mechanically interact with the arm by pushing it away (haptics) or by taking an object out of the robot's gripper (force sensing). The design objective has been to exploit the human operator's intuition by modeling the mechanical structure, the senses, and the behaviors of the assistant on human anatomy, human perception, and human motor behavior.
Schließen
doi:10.1109/IROS.2003.1249692
Schließen
80 Einträge « ‹ 1 von 2 › »