Prof. Dr. rer. nat. Ioannis Iossifidis

AUSGEWÄHLTE PUBLIKATIONEN

• 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 motor behavior human operators intuition human robot collaboration inverse kinematics Machine Learning man machine interaction man-machine-interaction manipulators mechanical structure modeling movement model object recognition pervasive design robot manipulator control robot vision robotic assistant sensory channels speech recognition user modelling

  108 Einträge « ‹ 1 von 11 › »

  2022

  108.

  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

  107.

  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

  106.

  Doliwa, Sebastian; Erbeslöh, Andreas; Seidl, Karsten; Iossifidis, Ioannis

  Development of a Scalable Analog Front-End for Brain-Computer Interfaces Konferenzbeitrag

  In: 17th International Conference on PhD Research in Microelectronics and Electronics, IEEE Prime 2022, Sardinia, Italy, 2022.

  BibTeX | Schlagwörter: BCI, Implantable BCI

  @inproceedings{doliwaDevelopmentScalableAnalogaccepted,
  
  title = {Development of a Scalable Analog Front-End for Brain-Computer Interfaces},
  
  author = {Sebastian Doliwa and Andreas Erbeslöh and Karsten Seidl and Ioannis Iossifidis},
  
  year  = {2022},
  
  date = {2022-06-15},
  
  urldate = {2022-06-15},
  
  booktitle = {17th International Conference on PhD Research in Microelectronics and Electronics},
  
  publisher = {IEEE Prime 2022},
  
  address = {Sardinia, Italy},
  
  keywords = {BCI, Implantable BCI},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  105.

  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

  104.

  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

  103.

  Fidencio, Aline Xavier; Glasmachers, Tobias; Iossifidis, Ioannis

  Error-Related Potentials Detection with Dry- and Wet-Electrode EEG Konferenzbeitrag

  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

  102.

  Schmidt, Marie Dominique; Glasmachers, Tobias; Iossifidis, Ioannis

  Motion Intention Prediction Konferenzbeitrag

  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

  101.

  Grün, Felix; Glasmachers, Tobias; Iossifidis, Ioannis

  Off-Policy Continuous Control Using Distributional Reinforcement Learning Konferenzbeitrag

  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

  100.

  Lehmler, Stephan Johann; Saif-ur-Rehman, Muhammad; Glasmachers, Tobias; Iossifidis, Ioannis

  Transfer-Learning for Patient Specific Model Re-Calibration: Application to sEMG-Classification Konferenzbeitrag

  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

  99.

  Schmidt, Marie Dominique; Glasmachers, Tobias; Iossifidis, Ioannis

  Artificially Generated Muscle Signals Konferenzbeitrag

  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

  108 Einträge « ‹ 1 von 11 › »