Understanding Brain Connectivity Patterns during Motor Imagery for 
Brain-Computer Interfacing

Grosse-Wentrup, M

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Understanding Brain Connectivity Patterns during Motor Imagery for Brain-Computer Interfacing

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Grosse-Wentrup, M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://papers.nips.cc/paper/3505-understanding-brain-connectivity-patterns-during-motor-imagery-for-brain-computer-interfacing.pdf
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Citation

Grosse-Wentrup, M. (2009). Understanding Brain Connectivity Patterns during Motor Imagery for Brain-Computer Interfacing. In D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou (Eds.), Advances in neural information processing systems 21 (pp. 561-568). Red Hook, NY, USA: Curran.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C4AB-7

Abstract

EEG connectivity measures could provide a new type of feature space for inferring a subject's intention in Brain-Computer Interfaces (BCIs). However, very little is known on EEG connectivity patterns for BCIs. In this study, EEG connectivity during motor imagery (MI) of the left and right is investigated in a broad frequency range across the whole scalp by combining Beamforming with Transfer Entropy and taking into account possible volume conduction effects. Observed connectivity patterns indicate that modulation intentionally induced by MI is strongest in the gamma-band, i.e., above 35 Hz. Furthermore, modulation between MI and rest is found to be more pronounced than between MI of different hands. This is in contrast to results on MI obtained with bandpower features, and might provide an explanation for the so far only moderate success of connectivity features in BCIs. It is concluded that future studies on connectivity based BCIs should focus on high frequency bands and con
side
r ex
peri
mental paradigms that maximally vary cognitive demands between conditions.