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 EEG 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 pervasive design robot manipulator control robot vision robotic assistant sensory channels user modelling

  159 Einträge « ‹ 1 von 16 › »

  2024

  159.

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

  Understanding Activation Patterns in Artificial Neural Networks by Exploring Stochastic Processes: Discriminating Generalization from Memorization Artikel

  In: Neurocomputing, S. 128473, 2024, ISSN: 0925-2312.

  Abstract | Links | BibTeX | Schlagwörter: Artificial neural networks, Generalization, Machine Learning, Memorization, Poisson process, Stochastic modeling

  @article{lehmlerUnderstandingActivationPatterns2024,
  
  title = {Understanding Activation Patterns in Artificial Neural Networks by Exploring Stochastic Processes: Discriminating Generalization from Memorization},
  
  author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},
  
  editor = {Elsevier},
  
  url = {https://www.sciencedirect.com/science/article/pii/S092523122401244X},
  
  doi = {10.1016/j.neucom.2024.128473},
  
  issn = {0925-2312},
  
  year  = {2024},
  
  date = {2024-09-19},
  
  urldate = {2024-09-19},
  
  journal = {Neurocomputing},
  
  pages = {128473},
  
  abstract = {To gain a deeper understanding of the behavior and learning dynamics of artificial neural networks, mathematical abstractions and models are valuable. They provide a simplified perspective and facilitate systematic investigations. In this paper, we propose to analyze dynamics of artificial neural activation using stochastic processes, which have not been utilized for this purpose thus far. Our approach involves modeling the activation patterns of nodes in artificial neural networks as stochastic processes. By focusing on the activation frequency, we can leverage techniques used in neuroscience to study neural spike trains. Specifically, we extract the activity of individual artificial neurons during a classification task and model their activation frequency. The underlying process model is an arrival process following a Poisson distribution.We examine the theoretical fit of the observed data generated by various artificial neural networks in image recognition tasks to the proposed model’s key assumptions. Through the stochastic process model, we derive measures describing activation patterns of each network. We analyze randomly initialized, generalizing, and memorizing networks, allowing us to identify consistent differences in learning methods across multiple architectures and training sets. We calculate features describing the distribution of Activation Rate and Fano Factor, which prove to be stable indicators of memorization during learning. These calculated features offer valuable insights into network behavior. The proposed model demonstrates promising results in describing activation patterns and could serve as a general framework for future investigations. It has potential applications in theoretical simulation studies as well as practical areas such as pruning or transfer learning.},
  
  keywords = {Artificial neural networks, Generalization, Machine Learning, Memorization, Poisson process, Stochastic modeling},
  
  pubstate = {published},
  
  tppubtype = {article}
  
  }
  
  

  Schließen

  To gain a deeper understanding of the behavior and learning dynamics of artificial neural networks, mathematical abstractions and models are valuable. They provide a simplified perspective and facilitate systematic investigations. In this paper, we propose to analyze dynamics of artificial neural activation using stochastic processes, which have not been utilized for this purpose thus far. Our approach involves modeling the activation patterns of nodes in artificial neural networks as stochastic processes. By focusing on the activation frequency, we can leverage techniques used in neuroscience to study neural spike trains. Specifically, we extract the activity of individual artificial neurons during a classification task and model their activation frequency. The underlying process model is an arrival process following a Poisson distribution.We examine the theoretical fit of the observed data generated by various artificial neural networks in image recognition tasks to the proposed model’s key assumptions. Through the stochastic process model, we derive measures describing activation patterns of each network. We analyze randomly initialized, generalizing, and memorizing networks, allowing us to identify consistent differences in learning methods across multiple architectures and training sets. We calculate features describing the distribution of Activation Rate and Fano Factor, which prove to be stable indicators of memorization during learning. These calculated features offer valuable insights into network behavior. The proposed model demonstrates promising results in describing activation patterns and could serve as a general framework for future investigations. It has potential applications in theoretical simulation studies as well as practical areas such as pruning or transfer learning.

  Schließen

  • https://www.sciencedirect.com/science/article/pii/S092523122401244X
  • doi:10.1016/j.neucom.2024.128473

  Schließen

  158.

  Lehmler, Stephan Johann; Iossifidis, Ioannis

  3D Movement Analysis of the Ruhr Hand Motion Catalog of Human Center-Out Transport Trajectories Proceedings Article

  In: BC24 : Computational Neuroscience & Neurotechnology Bernstein Conference 2024, BCCN Bernstein Network Computational Networkvphantom, 2024.

  Abstract | Links | BibTeX | Schlagwörter:

  @inproceedings{3DMovementAnalysis2024,
  
  title = {3D Movement Analysis of the Ruhr Hand Motion Catalog of Human Center-Out Transport Trajectories},
  
  author = {Stephan Johann Lehmler and Ioannis Iossifidis},
  
  url = {https://abstracts.g-node.org/conference/BC24/abstracts#/uuid/719bca6f-9fb9-4e53-96a5-a7b36b67c012},
  
  year  = {2024},
  
  date = {2024-09-18},
  
  urldate = {2024-09-18},
  
  booktitle = {BC24 : Computational Neuroscience & Neurotechnology Bernstein Conference 2024},
  
  publisher = {BCCN Bernstein Network Computational Networkvphantom},
  
  abstract = {The Ruhr Hand Motion Catalog of Human Center-Out Transport Trajectories [1] is a compilation of three-dimensional task-space motion data simultaneously measured by two motion tracking systems. The first one, an optical motion capture system, provided robust reference data. The second recording system consisted of a single state-of-the-art IMU to demonstrate the feasibility of portable applications. The transport object was moved in 3D space from a unified start position to one of nine target positions, equidistantly aligned on a semicircle. Ten trials were performed per target and hand, resulting in 180 trials per participant in total. 31 participants (11 female, 20 male, age 21-78) without known movement disorders took part in the experiment. Based on those experimental data, we analyze several characteristics of upper-limb trajectories. All data are rotated so that the straight connection of the defined start and target positions composes the y-axis. By doing so, we explore properties which are independent of the directly measured target location for each task and focus on common properties shared between all target movements. Particularly, we investigate how individual or target-dependent differences can still be quantified after rotation. Furthermore, we model the measured movements by means of dynamical systems (extended attractor dynamics). Differences between the transportation movements to different targets would result in varying parameter sets. The investigated motion characteristics include the symmetry of velocity peaks and the polynomial target dependence of planarity attributes. To compare the diversity of trajectories in time and space, we introduce a novel variability measure for the planarity of hand paths regarding plane angles and path amplitudes within the plane. These aspects can expose differences between trials (intra-subject) and participants (inter-subject), explored in the modelling process and applied as a methodological framework for pathological analysis. For this, further measurements with patients experiencing movement disorders are planned for future examination. The separability can also be evaluated by machine learning of task classification and user identification. This can provide information on the potential of data-driven pathological analysis to extend the model-based approach since the described experiment and study are conducted in the context of developing a portable glove for the diagnosis of movement disorders.},
  
  keywords = {},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  The Ruhr Hand Motion Catalog of Human Center-Out Transport Trajectories [1] is a compilation of three-dimensional task-space motion data simultaneously measured by two motion tracking systems. The first one, an optical motion capture system, provided robust reference data. The second recording system consisted of a single state-of-the-art IMU to demonstrate the feasibility of portable applications. The transport object was moved in 3D space from a unified start position to one of nine target positions, equidistantly aligned on a semicircle. Ten trials were performed per target and hand, resulting in 180 trials per participant in total. 31 participants (11 female, 20 male, age 21-78) without known movement disorders took part in the experiment. Based on those experimental data, we analyze several characteristics of upper-limb trajectories. All data are rotated so that the straight connection of the defined start and target positions composes the y-axis. By doing so, we explore properties which are independent of the directly measured target location for each task and focus on common properties shared between all target movements. Particularly, we investigate how individual or target-dependent differences can still be quantified after rotation. Furthermore, we model the measured movements by means of dynamical systems (extended attractor dynamics). Differences between the transportation movements to different targets would result in varying parameter sets. The investigated motion characteristics include the symmetry of velocity peaks and the polynomial target dependence of planarity attributes. To compare the diversity of trajectories in time and space, we introduce a novel variability measure for the planarity of hand paths regarding plane angles and path amplitudes within the plane. These aspects can expose differences between trials (intra-subject) and participants (inter-subject), explored in the modelling process and applied as a methodological framework for pathological analysis. For this, further measurements with patients experiencing movement disorders are planned for future examination. The separability can also be evaluated by machine learning of task classification and user identification. This can provide information on the potential of data-driven pathological analysis to extend the model-based approach since the described experiment and study are conducted in the context of developing a portable glove for the diagnosis of movement disorders.

  Schließen

  • https://abstracts.g-node.org/conference/BC24/abstracts#/uuid/719bca6f-9fb9-4e53-[...]

  Schließen

  157.

  Grün, Felix; Iossifidis, Ioannis

  Controversial Opinions on Model Based and Model Free Reinforcement Learning in the Brain Proceedings Article

  In: BCCN Bernstein Network Computational Networkvphantom, 2024.

  Abstract | Links | BibTeX | Schlagwörter: Machine Learning, Reinforcement learning

  @inproceedings{ControversialOpinionsModel2024,
  
  title = {Controversial Opinions on Model Based and Model Free Reinforcement Learning in the Brain},
  
  author = {Felix Grün and Ioannis Iossifidis},
  
  url = {https://abstracts.g-node.org/conference/BC24/abstracts#/uuid/18e92e07-e4b1-43af-b2ac-ea282f4e81e7},
  
  year  = {2024},
  
  date = {2024-09-18},
  
  urldate = {2024-09-24},
  
  publisher = {BCCN Bernstein Network Computational Networkvphantom},
  
  abstract = {Dopaminergic Reward Prediction Errors (RPEs) are a key motivation and inspiration for model free, temporal difference reinforcement learning methods. Originally, the correlation of RPEs with model free temporal difference errors was seen as a strong indicator for model free reinforcement learning in brains. The standard view was that model free learning is the norm and more computationally expensive model based decision-making is only used when it leads to outcomes that are good enough to justify the additional effort. Nowadays, the landscape of opinions, models and experimental evidence, both electrophysiological and behavioral, paints a more complex picture, including but not limited to mechanisms of arbitration between the two systems. Model based learning or hybrid models better capture experimental behavioral data, and model based signatures are found in RPEs that were previously thought to be model free or hybrid [1]. The evidence for clearly model free learning is scarce [2]. On the other hand, multiple approaches show how model based behavior and RPEs can be produced with fundamentally model free reinforcement learning methods [3, 4, 5]. We point out findings that seem to contradict each other, others that complement each other, speculate which ideas are compatible with each other and give our opinions on ways forward, towards understanding if and how model based and model free learning from rewards coexist and interact in the brain.},
  
  keywords = {Machine Learning, Reinforcement learning},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  Dopaminergic Reward Prediction Errors (RPEs) are a key motivation and inspiration for model free, temporal difference reinforcement learning methods. Originally, the correlation of RPEs with model free temporal difference errors was seen as a strong indicator for model free reinforcement learning in brains. The standard view was that model free learning is the norm and more computationally expensive model based decision-making is only used when it leads to outcomes that are good enough to justify the additional effort. Nowadays, the landscape of opinions, models and experimental evidence, both electrophysiological and behavioral, paints a more complex picture, including but not limited to mechanisms of arbitration between the two systems. Model based learning or hybrid models better capture experimental behavioral data, and model based signatures are found in RPEs that were previously thought to be model free or hybrid [1]. The evidence for clearly model free learning is scarce [2]. On the other hand, multiple approaches show how model based behavior and RPEs can be produced with fundamentally model free reinforcement learning methods [3, 4, 5]. We point out findings that seem to contradict each other, others that complement each other, speculate which ideas are compatible with each other and give our opinions on ways forward, towards understanding if and how model based and model free learning from rewards coexist and interact in the brain.

  Schließen

  • https://abstracts.g-node.org/conference/BC24/abstracts#/uuid/18e92e07-e4b1-43af-[...]

  Schließen

  156.

  Schmidt, Marie Dominique; Iossifidis, Ioannis

  Decoding Upper Limb Movements Proceedings Article

  In: BCCN Bernstein Network Computational Networkvphantom, 2024.

  Abstract | Links | BibTeX | Schlagwörter: BCI, Machine Learning, Muscle activity

  @inproceedings{DecodingUpperLimb2024,
  
  title = {Decoding Upper Limb Movements},
  
  author = {Marie Dominique Schmidt and Ioannis Iossifidis},
  
  url = {https://abstracts.g-node.org/conference/BC24/abstracts#/uuid/4725140f-ce7c-4ac5-b694-c627ceeb8d98},
  
  year  = {2024},
  
  date = {2024-09-18},
  
  urldate = {2024-09-24},
  
  publisher = {BCCN Bernstein Network Computational Networkvphantom},
  
  abstract = {The upper limbs are essential for performing everyday tasks that require a wide range of motion and precise coordination. Planning and timing are crucial to achieve coordinated movement. Sensory information about the target and current body state is critical, as is the integration of prior experience represented by prelearned inverse dynamics that generate the associated muscle activity. We propose a generative model that uses a recurrent neural network to predict upper limb muscle activity during various simple and complex everyday movements. By identifying movement primitives within the signal, our model enables the decomposition of these movements into a fundamental set, facilitating the reconstruction of muscle activity patterns. Our approach has implications for the fundamental understanding of movement control and the rehabilitation of neuromuscular disorders with myoelectric prosthetics and functional electrical stimulation.},
  
  keywords = {BCI, Machine Learning, Muscle activity},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  The upper limbs are essential for performing everyday tasks that require a wide range of motion and precise coordination. Planning and timing are crucial to achieve coordinated movement. Sensory information about the target and current body state is critical, as is the integration of prior experience represented by prelearned inverse dynamics that generate the associated muscle activity. We propose a generative model that uses a recurrent neural network to predict upper limb muscle activity during various simple and complex everyday movements. By identifying movement primitives within the signal, our model enables the decomposition of these movements into a fundamental set, facilitating the reconstruction of muscle activity patterns. Our approach has implications for the fundamental understanding of movement control and the rehabilitation of neuromuscular disorders with myoelectric prosthetics and functional electrical stimulation.

  Schließen

  • https://abstracts.g-node.org/conference/BC24/abstracts#/uuid/4725140f-ce7c-4ac5-[...]

  Schließen

  155.

  Lehmler, Stephan Johann; Iossifidis, Ioannis

  Stochastic Process Model Derived Indicators of Overfitting for Deep Architectures: Applicability to Small Sample Recalibration of sEMG Decoders Proceedings Article

  In: BC24 : Computational Neuroscience & Neurotechnology Bernstein Conference 2024, BCCN Bernstein Network Computational Networkvphantom, 2024.

  Abstract | Links | BibTeX | Schlagwörter: Machine Learning

  @inproceedings{StochasticProcessModel2024,
  
  title = {Stochastic Process Model Derived Indicators of Overfitting for Deep Architectures: Applicability to Small Sample Recalibration of sEMG Decoders},
  
  author = {Stephan Johann Lehmler and Ioannis Iossifidis},
  
  url = {https://abstracts.g-node.org/conference/BC24/abstracts#/uuid/72f03ff1-61dc-443c-92c2-b623d672ce15},
  
  year  = {2024},
  
  date = {2024-09-18},
  
  urldate = {2024-09-24},
  
  booktitle = {BC24 : Computational Neuroscience & Neurotechnology Bernstein Conference 2024},
  
  publisher = {BCCN Bernstein Network Computational Networkvphantom},
  
  abstract = {Our recent work presents a stochastic process model of the activations within an ANN and shows a promising indicator to distinguish memorizing from generalizing ANNs. The average λ, or mean firing rate (MFR), of a hidden layer, shows stable differences between memorizing and generalizing networks, comparatively independent of the underlying data used for evaluation. We first show the performance of this indicator during training on benchmark computer vision datasets such as MNIST and CIFAR-10. In a second step, we extend the work to the real-life use case of calibrating a pre-trained model to a new user. We focus on decoding surface electromyographic (sEMG) signals, which are highly variable within and between users, and therefore necessitate frequent user calibration. Especially in situations when user calibration has to only rely on a small number of samples, degradation in performance overtime due to memorization and overfitting is a not unlikely outcome. In those cases, traditional regularization methods that function by observing the performance on a validation set, such as early stopping, don’t necessarily work, because they are evaluated on data from the same subject and set of movements, which features are being memorized. Our new indicators of memorization could help as stable indicators for model performance and give live insights during model calibration when more samples from the new users would be necessary. We evaluate the usefulness of the MFR-indicator for identifying the moment a pre-trained sEMG decoder starts to memorize given inputs},
  
  keywords = {Machine Learning},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  Our recent work presents a stochastic process model of the activations within an ANN and shows a promising indicator to distinguish memorizing from generalizing ANNs. The average λ, or mean firing rate (MFR), of a hidden layer, shows stable differences between memorizing and generalizing networks, comparatively independent of the underlying data used for evaluation. We first show the performance of this indicator during training on benchmark computer vision datasets such as MNIST and CIFAR-10. In a second step, we extend the work to the real-life use case of calibrating a pre-trained model to a new user. We focus on decoding surface electromyographic (sEMG) signals, which are highly variable within and between users, and therefore necessitate frequent user calibration. Especially in situations when user calibration has to only rely on a small number of samples, degradation in performance overtime due to memorization and overfitting is a not unlikely outcome. In those cases, traditional regularization methods that function by observing the performance on a validation set, such as early stopping, don’t necessarily work, because they are evaluated on data from the same subject and set of movements, which features are being memorized. Our new indicators of memorization could help as stable indicators for model performance and give live insights during model calibration when more samples from the new users would be necessary. We evaluate the usefulness of the MFR-indicator for identifying the moment a pre-trained sEMG decoder starts to memorize given inputs

  Schließen

  • https://abstracts.g-node.org/conference/BC24/abstracts#/uuid/72f03ff1-61dc-443c-[...]

  Schließen

  154.

  Fidencio, Aline Xavier; Klaes, Christian; Iossifidis, Ioannis

  Adaptive Brain-Computer Interfaces Based on Error-Related Potentials and Reinforcement Learning Proceedings Article

  In: BC24 : Computational Neuroscience & Neurotechnology Bernstein Conference 2024, BCCN Bernstein Network Computational Networkvphantom, 2024.

  Abstract | Links | BibTeX | Schlagwörter: BCI, Machine Learning

  @inproceedings{AdaptiveBraincomputerInterfaces2024,
  
  title = {Adaptive Brain-Computer Interfaces Based on Error-Related Potentials and Reinforcement Learning},
  
  author = {Aline Xavier Fidencio and Christian Klaes and Ioannis Iossifidis},
  
  url = {https://abstracts.g-node.org/conference/BC24/abstracts#/uuid/03d3dd16-4c50-43d8-b878-abcfa7857386},
  
  year  = {2024},
  
  date = {2024-09-18},
  
  urldate = {2024-09-24},
  
  booktitle = {BC24 : Computational Neuroscience & Neurotechnology Bernstein Conference 2024},
  
  publisher = {BCCN Bernstein Network Computational Networkvphantom},
  
  abstract = {Error-related potentials (ErrPs) represent the neural signature of error processing in the brain and numerous studies have demonstrated their reliable detection using non-invasive techniques such as electroencephalography (EEG). Over recent decades, the brain-computer interface (BCI) community has shown growing interest in leveraging these intrinsic feedback signals to enhance system performance. However, the effective use of ErrPs in a closed-loop setup crucially depends on accurate single-trial detection, which is typically achieved using a subject-specific classifier (or decoder) trained on samples recorded during extensive calibration sessions before the BCI system can be deployed. In our research, we explore the potential of simulated EEG data for training a truly generic ErrP classifier. Utilizing the SEREEGA simulator, we demonstrate that EEG data can be generated in a cost-effective manner, allowing for controlled and systematic variations in data distribution to accommodate uncertainties in ErrP generation. A classifier trained solely on the generated data exhibits promising generalization capabilities across different datasets and performs comparably to a leave-one-subject-out approach trained on real data (Xavier Fidêncio et al., 2024). In our experiments, we deliberately provoked ErrPs when the BCI misinterpreted the user's intention, resulting in incorrect actions. Subjects engaged in a game controlled via keyboard and/or motor imagery (imagining hand movements), with EEG data recorded using various EEG systems for comparison. Considering the challenges in obtaining clear ErrP signals for all subjects and the limitations identified in existing literature (Xavier Fidêncio et al., 2022), we hypothesize whether a measurable error signal is consistently generated at the scalp level when subjects encounter erroneous conditions, and how this influences closed-loop setups that incorporate ErrPs for improved BCI performance. To address these questions, we assess the effects of the occurrence-to-detection ratio of ErrPs in the classification pipeline using simulated data and explore the impact of error misclassification rates in an ErrP-based learning framework, which employs reinforcement learning to enhance BCI performance.},
  
  keywords = {BCI, Machine Learning},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  Error-related potentials (ErrPs) represent the neural signature of error processing in the brain and numerous studies have demonstrated their reliable detection using non-invasive techniques such as electroencephalography (EEG). Over recent decades, the brain-computer interface (BCI) community has shown growing interest in leveraging these intrinsic feedback signals to enhance system performance. However, the effective use of ErrPs in a closed-loop setup crucially depends on accurate single-trial detection, which is typically achieved using a subject-specific classifier (or decoder) trained on samples recorded during extensive calibration sessions before the BCI system can be deployed. In our research, we explore the potential of simulated EEG data for training a truly generic ErrP classifier. Utilizing the SEREEGA simulator, we demonstrate that EEG data can be generated in a cost-effective manner, allowing for controlled and systematic variations in data distribution to accommodate uncertainties in ErrP generation. A classifier trained solely on the generated data exhibits promising generalization capabilities across different datasets and performs comparably to a leave-one-subject-out approach trained on real data (Xavier Fidêncio et al., 2024). In our experiments, we deliberately provoked ErrPs when the BCI misinterpreted the user's intention, resulting in incorrect actions. Subjects engaged in a game controlled via keyboard and/or motor imagery (imagining hand movements), with EEG data recorded using various EEG systems for comparison. Considering the challenges in obtaining clear ErrP signals for all subjects and the limitations identified in existing literature (Xavier Fidêncio et al., 2022), we hypothesize whether a measurable error signal is consistently generated at the scalp level when subjects encounter erroneous conditions, and how this influences closed-loop setups that incorporate ErrPs for improved BCI performance. To address these questions, we assess the effects of the occurrence-to-detection ratio of ErrPs in the classification pipeline using simulated data and explore the impact of error misclassification rates in an ErrP-based learning framework, which employs reinforcement learning to enhance BCI performance.

  Schließen

  • https://abstracts.g-node.org/conference/BC24/abstracts#/uuid/03d3dd16-4c50-43d8-[...]

  Schließen

  153.

  Pilacinski, Artur; Christ, Lukas; Boshoff, Marius; Iossifidis, Ioannis; Adler, Patrick; Miro, Michael; Kuhlenkötter, Bernd; Klaes, Christian

  Human in the Collaborative Loop: A Strategy for Integrating Human Activity Recognition and Non-Invasive Brain-Machine Interfaces to Control Collaborative Robots Artikel

  In: Frontiers in Neurorobotics, Bd. 18, 2024, ISSN: 1662-5218.

  Links | BibTeX | Schlagwörter: brain-machine interfaces, EEG, Human action recognition, human-robot collaboration, Sensor Fusion

  @article{pilacinskiHumanCollaborativeLoop2024,
  
  title = {Human in the Collaborative Loop: A Strategy for Integrating Human Activity Recognition and Non-Invasive Brain-Machine Interfaces to Control Collaborative Robots},
  
  author = {Artur Pilacinski and Lukas Christ and Marius Boshoff and Ioannis Iossifidis and Patrick Adler and Michael Miro and Bernd Kuhlenkötter and Christian Klaes},
  
  url = {https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2024.1383089/full},
  
  doi = {10.3389/fnbot.2024.1383089},
  
  issn = {1662-5218},
  
  year  = {2024},
  
  date = {2024-09-18},
  
  urldate = {2024-09-18},
  
  journal = {Frontiers in Neurorobotics},
  
  volume = {18},
  
  publisher = {Frontiers},
  
  keywords = {brain-machine interfaces, EEG, Human action recognition, human-robot collaboration, Sensor Fusion},
  
  pubstate = {published},
  
  tppubtype = {article}
  
  }
  
  

  Schließen

  • https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2024.1[...]
  • doi:10.3389/fnbot.2024.1383089

  Schließen

  152.

  Fidêncio, Aline Xavier; Klaes, Christian; Iossifidis, Ioannis

  A Generic Error-Related Potential Classifier Based on Simulated Subjects Artikel

  In: Frontiers in Human Neuroscience, Bd. 18, S. 1390714, 2024, ISSN: 1662-5161.

  Abstract | Links | BibTeX | Schlagwörter: adaptive brain-machine (computer) interface, BCI, EEG, Error-related potential (ErrP), ErrP classifier, Generic decoder, Machine Learning, SEREEGA, Simulation

  @article{xavierfidencioGenericErrorrelatedPotential2024,
  
  title = {A Generic Error-Related Potential Classifier Based on Simulated Subjects},
  
  author = {Aline Xavier Fidêncio and Christian Klaes and Ioannis Iossifidis},
  
  editor = {Frontiers Media SA},
  
  url = {https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2024.1390714/full},
  
  doi = {10.3389/fnhum.2024.1390714},
  
  issn = {1662-5161},
  
  year  = {2024},
  
  date = {2024-07-19},
  
  urldate = {2024-07-17},
  
  journal = {Frontiers in Human Neuroscience},
  
  volume = {18},
  
  pages = {1390714},
  
  publisher = {Frontiers},
  
  abstract = {$<$p$>$Error-related potentials (ErrPs) are brain signals known to be generated as a reaction to erroneous events. Several works have shown that not only self-made errors but also mistakes generated by external agents can elicit such event-related potentials. The possibility of reliably measuring ErrPs through non-invasive techniques has increased the interest in the brain-computer interface (BCI) community in using such signals to improve performance, for example, by performing error correction. Extensive calibration sessions are typically necessary to gather sufficient trials for training subject-specific ErrP classifiers. This procedure is not only time-consuming but also boresome for participants. In this paper, we explore the effectiveness of ErrPs in closed-loop systems, emphasizing their dependency on precise single-trial classification. To guarantee the presence of an ErrPs signal in the data we employ and to ensure that the parameters defining ErrPs are systematically varied, we utilize the open-source toolbox SEREEGA for data simulation. We generated training instances and evaluated the performance of the generic classifier on both simulated and real-world datasets, proposing a promising alternative to conventional calibration techniques. Results show that a generic support vector machine classifier reaches balanced accuracies of 72.9%, 62.7%, 71.0%, and 70.8% on each validation dataset. While performing similarly to a leave-one-subject-out approach for error class detection, the proposed classifier shows promising generalization across different datasets and subjects without further adaptation. Moreover, by utilizing SEREEGA, we can systematically adjust parameters to accommodate the variability in the ErrP, facilitating the systematic validation of closed-loop setups. Furthermore, our objective is to develop a universal ErrP classifier that captures the signal's variability, enabling it to determine the presence or absence of an ErrP in real EEG data.$<$/p$>$},
  
  keywords = {adaptive brain-machine (computer) interface, BCI, EEG, Error-related potential (ErrP), ErrP classifier, Generic decoder, Machine Learning, SEREEGA, Simulation},
  
  pubstate = {published},
  
  tppubtype = {article}
  
  }
  
  

  Schließen

  $<$p$>$Error-related potentials (ErrPs) are brain signals known to be generated as a reaction to erroneous events. Several works have shown that not only self-made errors but also mistakes generated by external agents can elicit such event-related potentials. The possibility of reliably measuring ErrPs through non-invasive techniques has increased the interest in the brain-computer interface (BCI) community in using such signals to improve performance, for example, by performing error correction. Extensive calibration sessions are typically necessary to gather sufficient trials for training subject-specific ErrP classifiers. This procedure is not only time-consuming but also boresome for participants. In this paper, we explore the effectiveness of ErrPs in closed-loop systems, emphasizing their dependency on precise single-trial classification. To guarantee the presence of an ErrPs signal in the data we employ and to ensure that the parameters defining ErrPs are systematically varied, we utilize the open-source toolbox SEREEGA for data simulation. We generated training instances and evaluated the performance of the generic classifier on both simulated and real-world datasets, proposing a promising alternative to conventional calibration techniques. Results show that a generic support vector machine classifier reaches balanced accuracies of 72.9%, 62.7%, 71.0%, and 70.8% on each validation dataset. While performing similarly to a leave-one-subject-out approach for error class detection, the proposed classifier shows promising generalization across different datasets and subjects without further adaptation. Moreover, by utilizing SEREEGA, we can systematically adjust parameters to accommodate the variability in the ErrP, facilitating the systematic validation of closed-loop setups. Furthermore, our objective is to develop a universal ErrP classifier that captures the signal's variability, enabling it to determine the presence or absence of an ErrP in real EEG data.$<$/p$>$

  Schließen

  • https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2[...]
  • doi:10.3389/fnhum.2024.1390714

  Schließen

  151.

  Ali, Omair; Saif-ur-Rehman, Muhammad; Metzler, Marita; Glasmachers, Tobias; Iossifidis, Ioannis; Klaes, Christian

  GET: A Generative EEG Transformer for Continuous Context-Based Neural Signals Artikel

  In: arXiv:2406.03115 [q-bio], 2024.

  Abstract | Links | BibTeX | Schlagwörter: BCI, EEG, Machine Learning, Quantitative Biology - Neurons and Cognition

  @article{aliGETGenerativeEEG2024,
  
  title = {GET: A Generative EEG Transformer for Continuous Context-Based Neural Signals},
  
  author = {Omair Ali and Muhammad Saif-ur-Rehman and Marita Metzler and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},
  
  url = {http://arxiv.org/abs/2406.03115},
  
  doi = {10.48550/arXiv.2406.03115},
  
  year  = {2024},
  
  date = {2024-06-09},
  
  urldate = {2024-06-09},
  
  journal = {arXiv:2406.03115 [q-bio]},
  
  abstract = {Generating continuous electroencephalography (EEG) signals through advanced artificial neural networks presents a novel opportunity to enhance brain-computer interface (BCI) technology. This capability has the potential to significantly enhance applications ranging from simulating dynamic brain activity and data augmentation to improving real-time epilepsy detection and BCI inference. By harnessing generative transformer neural networks, specifically designed for EEG signal generation, we can revolutionize the interpretation and interaction with neural data. Generative AI has demonstrated significant success across various domains, from natural language processing (NLP) and computer vision to content creation in visual arts and music. It distinguishes itself by using large-scale datasets to construct context windows during pre-training, a technique that has proven particularly effective in NLP, where models are fine-tuned for specific downstream tasks after extensive foundational training. However, the application of generative AI in the field of BCIs, particularly through the development of continuous, context-rich neural signal generators, has been limited. To address this, we introduce the Generative EEG Transformer (GET), a model leveraging transformer architecture tailored for EEG data. The GET model is pre-trained on diverse EEG datasets, including motor imagery and alpha wave datasets, enabling it to produce high-fidelity neural signals that maintain contextual integrity. Our empirical findings indicate that GET not only faithfully reproduces the frequency spectrum of the training data and input prompts but also robustly generates continuous neural signals. By adopting the successful training strategies of the NLP domain for BCIs, the GET sets a new standard for the development and application of neural signal generation technologies.},
  
  keywords = {BCI, EEG, Machine Learning, Quantitative Biology - Neurons and Cognition},
  
  pubstate = {published},
  
  tppubtype = {article}
  
  }
  
  

  Schließen

  Generating continuous electroencephalography (EEG) signals through advanced artificial neural networks presents a novel opportunity to enhance brain-computer interface (BCI) technology. This capability has the potential to significantly enhance applications ranging from simulating dynamic brain activity and data augmentation to improving real-time epilepsy detection and BCI inference. By harnessing generative transformer neural networks, specifically designed for EEG signal generation, we can revolutionize the interpretation and interaction with neural data. Generative AI has demonstrated significant success across various domains, from natural language processing (NLP) and computer vision to content creation in visual arts and music. It distinguishes itself by using large-scale datasets to construct context windows during pre-training, a technique that has proven particularly effective in NLP, where models are fine-tuned for specific downstream tasks after extensive foundational training. However, the application of generative AI in the field of BCIs, particularly through the development of continuous, context-rich neural signal generators, has been limited. To address this, we introduce the Generative EEG Transformer (GET), a model leveraging transformer architecture tailored for EEG data. The GET model is pre-trained on diverse EEG datasets, including motor imagery and alpha wave datasets, enabling it to produce high-fidelity neural signals that maintain contextual integrity. Our empirical findings indicate that GET not only faithfully reproduces the frequency spectrum of the training data and input prompts but also robustly generates continuous neural signals. By adopting the successful training strategies of the NLP domain for BCIs, the GET sets a new standard for the development and application of neural signal generation technologies.

  Schließen

  • http://arxiv.org/abs/2406.03115
  • doi:10.48550/arXiv.2406.03115

  Schließen

  2023

  150.

  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

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