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
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LEHRVERANSTALTUNGEN
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PROJEKTE
- Projekt mit Verlinkung
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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
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2023
1.Ali, Omair; Saif-ur-Rehman, Muhammad; Glasmachers, Tobias; Iossifidis, Ioannis; Klaes, Christian
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}
}
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.