Prof. Dr. rer. nat. Ioannis Iossifidis

AUSGEWÄHLTE PUBLIKATIONEN

• anthropomorphic robot arm arm movement model artificial skin attractor dynamics approach Autonomous robotics BCI behavior generation direct physical interaction Dynamical systems grippers haptic interface human anatomy human perception human robot collaboration inverse kinematics Machine Learning man machine interaction man-machine systems man-machine-interaction manipulators movement model object recognition operators gaze operators gesture Reinforcement learning robot manipulator control robotic assistant robots gripper sensory channels speech recognition

  120 Einträge « ‹ 1 von 12 › »

  2022

  120.

  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

  119.

  Grün, Felix; Iossifidis, Ioannis

  Exploring Distribution Parameterizations for Distributional Continuous Control Konferenzbeitrag

  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

  118.

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

  Modeling Subject Specfic Surface EMG Features by Means of Deep Learning Konferenzbeitrag

  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

  117.

  Schmidt, Marie Dominique; Iossifidis, Ioannis

  Linking Muscle Activity and Motion Trajectory Konferenzbeitrag

  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

  116.

  Sziburis, Tim; Blex, Susanne; Iossifidis, Ioannis

  A Dataset of 3D Hand Transport Trajectories Determined by Inertial Measurements from a Single Sensor Konferenzbeitrag

  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

  115.

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

  Closed-Loop Adaptation of Brain-Machine Interfaces Using Error-Related Potentials and Reinforcement Learning Konferenzbeitrag

  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

  114.

  Doliwa, Sebastian; Hussain, Muhammad Ayaz; Sziburis, Tim; Iossifidis, Ioannis

  Biologically Inspired Model for Timed Motion in Robotic Systems Konferenzbeitrag

  In: 9th IEEE RAS/EMBS International Conference on Biomedical Robotics & Biomechatronics, IEEE, Seoul, South Korea, 2022.

  BibTeX | Schlagwörter: Autonomous robotics, Dynamical systems

  @inproceedings{doliwaBiologicallyInspiredModel2022,
  
  title = {Biologically Inspired Model for Timed Motion in Robotic Systems},
  
  author = {Sebastian Doliwa and Muhammad Ayaz Hussain and Tim Sziburis and Ioannis Iossifidis},
  
  year  = {2022},
  
  date = {2022-08-12},
  
  urldate = {2022-08-12},
  
  booktitle = {9th IEEE RAS/EMBS International Conference on Biomedical Robotics & Biomechatronics},
  
  publisher = {IEEE},
  
  address = {Seoul, South Korea},
  
  keywords = {Autonomous robotics, Dynamical systems},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  113.

  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

  112.

  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

  111.

  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

  120 Einträge « ‹ 1 von 12 › »