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Computational Intelligence and Neuroscience
Volume 2007, Article ID 56986, 8 pages
Research Article

Modern Electrophysiological Methods for Brain-Computer Interfaces

1Electrical Neuroimaging Group, Department of Clinical Neurosciences, Geneva University Hospital, Geneva 1211, Switzerland
2Neurodynamics Laboratory, Department of Psychiatry and Clinical Psychobiology, University of Barcelona, Barcelona, Catalonia 08035, Spain
3Neurofisiopatologia Clinica, Fondazione Santa Lucia, Roma 00179, Italy

Received 15 February 2007; Revised 6 July 2007; Accepted 18 September 2007

Academic Editor: Andrzej Cichocki

Copyright © 2007 Rolando Grave de Peralta Menendez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Modern electrophysiological studies in animals show that the spectrum of neural oscillations encoding relevant information is broader than previously thought and that many diverse areas are engaged for very simple tasks. However, EEG-based brain-computer interfaces (BCI) still employ as control modality relatively slow brain rhythms or features derived from preselected frequencies and scalp locations. Here, we describe the strategy and the algorithms we have developed for the analysis of electrophysiological data and demonstrate their capacity to lead to faster accurate decisions based on linear classifiers. To illustrate this strategy, we analyzed two typical BCI tasks. (1) Mu-rhythm control of a cursor movement by a paraplegic patient. For this data, we show that although the patient received extensive training in mu-rhythm control, valuable information about movement imagination is present on the untrained high-frequency rhythms. This is the first demonstration of the importance of high-frequency rhythms in imagined limb movements. (2) Self-paced finger tapping task in three healthy subjects including the data set used in the BCI-2003 competition. We show that by selecting electrodes and frequency ranges based on their discriminative power, the classification rates can be systematically improved with respect to results published thus far.