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 force sensing grippers haptic interface human anatomy human motor behavior human perception inverse kinematics Machine Learning man machine interaction man-machine systems man-machine-interaction manipulators movement model object recognition operators gaze operators gesture pervasive design robot manipulator control robotic assistant robots gripper sensory channels

  150 Einträge « ‹ 3 von 15 › »

  2022

  130.

  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

  129.

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

  Error-Related Potentials Detection with Dry- and Wet-Electrode EEG Proceedings Article

  In: FENS, Forum 2022, FENS, Federation of European Neuroscience Societies, 2022.

  Abstract | BibTeX | Schlagwörter: BCI, EEG, error-related potentials, Machine Learning

  @inproceedings{fidencioErrorrelatedPotentialsDetection2022,
  
  title = {Error-Related Potentials Detection with Dry- and Wet-Electrode EEG},
  
  author = {Aline Xavier Fidencio and Tobias Glasmachers and Ioannis Iossifidis},
  
  year  = {2022},
  
  date = {2022-01-01},
  
  urldate = {2022-01-01},
  
  booktitle = {FENS, Forum 2022},
  
  publisher = {FENS, Federation of European Neuroscience Societies},
  
  abstract = {Electroencephalography (EEG) is a non-invasive technique for measuring brain electrical activity from electrodes placed on the scalp surface. Improvements in this technology are particularly relevant because they also boost brain-machine interfaces (BMI) development. Commonly, gel-based electrodes are used since they guarantee a high-quality signal. Alternatively, dry electrodes have been introduced, more suitable for daily use. In this work, we compare conventional dry and wet electrode systems specifically for the detection of error-related potentials (ErrPs). ErrPs are elicited as a reaction to both self-made and external errors. There has been increased interest in the integration of these signals into BMIs to improve their performance since they provide a convenient source of feedback to the system with no extra workload for the subject. These signals can be used, e.g., to correct errors or even for system adaptation. ErrP-based BMIs in the literature have consistently used wet electrodes. Therefore, even though both electrodes types have been compared for other event-related potentials (e.g., P300), it is relevant to know whether the signal quality for the detection of ErrPs is comparable among them. In this work, we implement a simple game to elicit ErrPs and compare the quality of the measured signals. We tested the feasibility of the experimental protocol to elicit ErrP and the measured ErrP displayed a similar waveshape in terms of observed peaks. However, differences exist in both latencies as well as in their amplitude. These variations and other relevant characteristics have to be further verified with more subjects},
  
  keywords = {BCI, EEG, error-related potentials, Machine Learning},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

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

  Schließen

  128.

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

  Motion Intention Prediction Proceedings Article

  In: FENS, Forum 2022, FENS, Federation of European Neuroscience Societies, 2022.

  Abstract | BibTeX | Schlagwörter: BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network

  @inproceedings{schmidtMotionIntentionPrediction2022a,
  
  title = {Motion Intention Prediction},
  
  author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},
  
  year  = {2022},
  
  date = {2022-01-01},
  
  urldate = {2022-01-01},
  
  booktitle = {FENS, Forum 2022},
  
  publisher = {FENS, Federation of European Neuroscience Societies},
  
  abstract = {Motion intention prediction is the key to robot-assisted rehabilitation systems. These can rely on various biological signals. One commonly used signal is the muscle activity measured by an electromyogram that occurs between 50-100 milliseconds before the actual movement, allowing a real-world application to assist in time. We show that upper limb motion can be estimated from the corresponding muscle activity. To this end, eight-arm muscles are mapped to the joint angle, velocity, and acceleration of the shoulder, elbow, and wrist. For this purpose, we specifically develop an artificial neural network that estimates complex motions involving multiple upper limb joints. The network model is evaluated concerning its ability to generalize across subjects as well as for new motions. This is achieved through training on multiple subjects and additional transfer learning methods so that the prediction for new subjects is significantly improved. In particular, this is beneficial for a robust real-world application. Furthermore, we investigate the importance of the different parameters such as angle, velocity, and acceleration for simple and complex motions. Predictions for simple motions along with the main components of complex motions achieve excellent accuracy while joints that do not play a dominant role during the motion have comparatively lower accuracy.},
  
  keywords = {BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

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

  Schließen

  2021

  127.

  Grün, Felix; Glasmachers, Tobias; Iossifidis, Ioannis

  Off-Policy Continuous Control Using Distributional Reinforcement Learning Proceedings Article

  In: Bernstein Conference, 2021.

  Links | BibTeX | Schlagwörter: Machine Learning, Reinforcement learning

  @inproceedings{grunOffPolicyContinuousControl2021b,
  
  title = {Off-Policy Continuous Control Using Distributional Reinforcement Learning},
  
  author = {Felix Grün and Tobias Glasmachers and Ioannis Iossifidis},
  
  doi = {10.12751/nncn.bc2021.p001},
  
  year  = {2021},
  
  date = {2021-10-01},
  
  urldate = {2021-10-01},
  
  publisher = {Bernstein Conference},
  
  keywords = {Machine Learning, Reinforcement learning},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  • doi:10.12751/nncn.bc2021.p001

  Schließen

  126.

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

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

  In: Bernstein Conferen, 2021.

  Links | BibTeX | Schlagwörter: BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning, transfer learning

  @inproceedings{lehmlerTransferLearningPatientSpecific2021b,
  
  title = {Transfer-Learning for Patient Specific Model Re-Calibration: Application to sEMG-Classification},
  
  author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},
  
  doi = {10.12751/nncn.bc2021.p005},
  
  year  = {2021},
  
  date = {2021-10-01},
  
  urldate = {2021-10-01},
  
  publisher = {Bernstein Conferen},
  
  keywords = {BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning, transfer learning},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  • doi:10.12751/nncn.bc2021.p005

  Schließen

  125.

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

  Artificially Generated Muscle Signals Proceedings Article

  In: Bernstein Conference, 2021.

  Links | BibTeX | Schlagwörter: BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network

  @inproceedings{schmidtArtificiallyGeneratedMuscle2021b,
  
  title = {Artificially Generated Muscle Signals},
  
  author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},
  
  doi = {10.12751/nncn.bc2021.p111},
  
  year  = {2021},
  
  date = {2021-10-01},
  
  urldate = {2021-10-01},
  
  publisher = {Bernstein Conference},
  
  keywords = {BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  • doi:10.12751/nncn.bc2021.p111

  Schließen

  124.

  Sziburis, Tim; Blex, Susanne; Glasmachers, Tobias; Rano, Inaki; Iossifidis, Ioannis

  Modelling the Generation of Human Upper-Limb Reaching Trajectories: An Extended Behavioural Attractor Dynamics Approach Proceedings Article

  In: Bernstein Conference, 2021.

  Links | BibTeX | Schlagwörter: attractor dynamics approach, human arm motion

  @inproceedings{sziburisModellingGenerationHuman2021b,
  
  title = {Modelling the Generation of Human Upper-Limb Reaching Trajectories: An Extended Behavioural Attractor Dynamics Approach},
  
  author = {Tim Sziburis and Susanne Blex and Tobias Glasmachers and Inaki Rano and Ioannis Iossifidis},
  
  doi = {10.12751/nncn.bc2021.p078},
  
  year  = {2021},
  
  date = {2021-10-01},
  
  urldate = {2021-10-01},
  
  publisher = {Bernstein Conference},
  
  keywords = {attractor dynamics approach, human arm motion},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  • doi:10.12751/nncn.bc2021.p078

  Schließen

  123.

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

  Beyond Error Correction: Integration of Error-Related Potentials into Brain-Computer Interfaces for Improved Performance Proceedings Article

  In: Bernstein Conference, 2021.

  Links | BibTeX | Schlagwörter: BCI, error-related potentials, Machine Learning, Reinforcement learning

  @inproceedings{xavierfidencioErrorCorrectionIntegration2021b,
  
  title = {Beyond Error Correction: Integration of Error-Related Potentials into Brain-Computer Interfaces for Improved Performance},
  
  author = {Aline Xavier Fidencio and Tobias Glasmachers and Christian Klaes and Ioannis Iossifidis},
  
  doi = {10.12751/nncn.bc2021.p163},
  
  year  = {2021},
  
  date = {2021-10-01},
  
  urldate = {2021-10-01},
  
  publisher = {Bernstein Conference},
  
  keywords = {BCI, error-related potentials, Machine Learning, Reinforcement learning},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  • doi:10.12751/nncn.bc2021.p163

  Schließen

  122.

  Grün, Felix; Glasmachers, Tobias; Iossifidis, Ioannis

  Off-Policy Continuous Control Using Distributional Reinforcement Learning Proceedings Article

  In: BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021, BCCN Bernstein Network Computational Network, 2021.

  Links | BibTeX | Schlagwörter: Machine Learning, Reinforcement learning

  @inproceedings{grunOffPolicyContinuousControl2021,
  
  title = {Off-Policy Continuous Control Using Distributional Reinforcement Learning},
  
  author = {Felix Grün and Tobias Glasmachers and Ioannis Iossifidis},
  
  doi = {10.12751/nncn.bc2021.p001},
  
  year  = {2021},
  
  date = {2021-09-15},
  
  urldate = {2021-09-15},
  
  booktitle = {BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021},
  
  publisher = {BCCN Bernstein Network Computational Network},
  
  keywords = {Machine Learning, Reinforcement learning},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  • doi:10.12751/nncn.bc2021.p001

  Schließen

  121.

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

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

  In: BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021, BCCN Bernstein Network Computational Network, 2021.

  Links | BibTeX | Schlagwörter: BCI, Machine Learning

  @inproceedings{lehmlerTransferLearningPatientSpecific2021,
  
  title = {Transfer-Learning for Patient Specific Model Re-Calibration: Application to sEMG-Classification},
  
  author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},
  
  doi = {10.12751/nncn.bc2021.p005},
  
  year  = {2021},
  
  date = {2021-09-15},
  
  urldate = {2021-09-15},
  
  booktitle = {BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021},
  
  publisher = {BCCN Bernstein Network Computational Network},
  
  keywords = {BCI, Machine Learning},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  • doi:10.12751/nncn.bc2021.p005

  Schließen

  150 Einträge « ‹ 3 von 15 › »