PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Understanding Brain Connectivity Patterns during Motor Imagery for Brain-Computer Interfacing
Moritz Grosse-Wentrup
In: Neural Information Processing Systems (NIPS) 2008, 8-10 Dec 2008, Vancouver, B.C., Canada.

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 consider experimental paradigms that maximally vary cognitive demands between conditions.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:4692
Deposited By:Moritz Grosse-Wentrup
Deposited On:24 March 2009