Computational Intelligence and Neuroscience

Computational Intelligence and Neuroscience / 2007 / Article
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Brain-Computer Interfaces: Towards Practical Implementations and Potential Applications

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Research Article | Open Access

Volume 2007 |Article ID 056986 | https://doi.org/10.1155/2007/56986

Rolando Grave de Peralta Menendez, Quentin Noirhomme, Febo Cincotti, Donatella Mattia, Fabio Aloise, Sara González Andino, "Modern Electrophysiological Methods for Brain-Computer Interfaces", Computational Intelligence and Neuroscience, vol. 2007, Article ID 056986, 8 pages, 2007. https://doi.org/10.1155/2007/56986

Modern Electrophysiological Methods for Brain-Computer Interfaces

Academic Editor: Andrzej Cichocki
Received15 Feb 2007
Revised06 Jul 2007
Accepted18 Sep 2007
Published21 Nov 2007

Abstract

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.

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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.


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