Lehrgebiet: Theoretische Informatik und künstliche Intelligenz
Büro: 01.214
Labor: 04.105
Telefon: +49 208 88254-806
E-Mail:
Ioannis Iossifidis studierte Physik (Schwerpunkt: theoretische Teilchenphysik) an der Universität Dortmund und promovierte 2006 an der Fakultät für Physik und Astronomie der Ruhr-Universität Bochum.
Am Institut für Neuroinformatik leitete Prof. Dr. Iossifidis die Arbeitsgruppe Autonome Robotik und nahm mit seiner Forschungsgruppe erfolgreich an zahlreichen, vom BmBF und der EU, geförderten Forschungsprojekten aus dem Bereich der künstlichen Intelligenz teil. Seit dem 1. Oktober 2010 arbeitet er an der HRW am Institut Informatik und hält den Lehrstuhl für Theoretische Informatik – Künstliche Intelligenz.
Prof. Dr. Ioannis Iossifidis entwickelt seit über 20 Jahren biologisch inspirierte anthropomorphe, autonome Robotersysteme, die zugleich Teil und Ergebnis seiner Forschung im Bereich der rechnergestützten Neurowissenschaften sind. In diesem Rahmen entwickelte er Modelle zur Informationsverarbeitung im menschlichen Gehirn und wendete diese auf technische Systeme an.
Ausgewiesene Schwerpunkte seiner wissenschaftlichen Arbeit der letzten Jahre sind die Modellierung menschlicher Armbewegungen, der Entwurf von sogenannten «Simulierten Realitäten» zur Simulation und Evaluation der Interaktionen zwischen Mensch, Maschine und Umwelt sowie die Entwicklung von kortikalen exoprothetischen Komponenten. Entwicklung der Theorie und Anwendung von Algorithmen des maschinellen Lernens auf Basis tiefer neuronaler Architekturen bilden das Querschnittsthema seiner Forschung.
Ioannis Iossifidis’ Forschung wurde u.a. mit Fördermitteln im Rahmen großer Förderprojekte des BmBF (NEUROS, MORPHA, LOKI, DESIRE, Bernstein Fokus: Neuronale Grundlagen des Lernens etc.), der DFG («Motor‐parietal cortical neuroprosthesis with somatosensory feedback for restoring hand and arm functions in tetraplegic patients») und der EU (Neural Dynamics – EU (STREP), EUCogII, EUCogIII ) honoriert und gehört zu den Gewinnern der Leitmarktwettbewerbe Gesundheit.NRW und IKT.NRW 2019.
ARBEITS- UND FORSCHUNGSSCHWERPUNKTE
- Computational Neuroscience
- Brain Computer Interfaces
- Entwicklung kortikaler exoprothetischer Komponenten
- Theorie neuronaler Netze
- Modellierung menschlicher Armbewegungen
- Simulierte Realität
WISSENSCHAFTLICHE EINRICHTUNGEN
- Labor mit Verlinkung
- ???
- ???
LEHRVERANSTALTUNGEN
- ???
- ???
- ???
PROJEKTE
- Projekt mit Verlinkung
- ???
- ???
WISSENSCHAFTLICHE MITARBEITER*INNEN
Felix Grün
Büro: 02.216 (Campus Bottrop)
Marie Schmidt
Büro: 02.216 (Campus Bottrop)
Aline Xavier Fidencio
Gastwissenschaftlerin
Muhammad Ayaz Hussain
Doktorand
Tim Sziburis
Doktorand
Farhad Rahmat
studentische Hilfskraft
AUSGEWÄHLTE PUBLIKATIONEN
-
2022
3.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}
}
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.2.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}
}
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 subjects2021
1.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}
}