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Title

Modern electrophysiological methods for brain-computer interfaces

Authors
Grave de Peralta Menendez, Rolando
Noirhomme, Quentin
Cincotti, Febo
Mattia, Donatella
Aloise, Fabio
Published in Computational Intelligence and Neuroscience. 2007, vol. 2007, no. 56986, p. 8
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.
Keywords BCIBMIBrain machine interfaceBrain computer interfaceElectrophysiologyNeural oscillations
Stable URL https://archive-ouverte.unige.ch/unige:16073
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Identifiers
PMID: 18288256
Structures
Research group Groupe Gonzales Andino Sara (The electrical neuroimaging) (868)

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Deposited on : 2011-06-06

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