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Parallel space-time-frequency decomposition of EEG signals for brain computer interfacing

Nazarpour, K, Sanei, S, Shoker, L and Chambers, JA (2006) Parallel space-time-frequency decomposition of EEG signals for brain computer interfacing European Signal Processing Conference.

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Abstract

The presented paper proposes a hybrid parallel factor analysis-support vector machines (PARAFAC-SVM) method for left and right index imagery movements classification. The spatial-temporal-spectral characteristics of the single trial electroencephalogram (EEG) signal are jointly considered. Within this novel scheme, we develop a parallel EEG space-time-frequency (STF) decomposition in μ band (8-13 Hz) at the preprocessing stage of the BCI system. Using PARAFAC, we elaborate two distinct factors in μ band for each EEG trial. SVM classifier is utilised to classify the spatial distribution of the movement related factor. This factor is distinguished by its spectral, temporal, and spatial distribution.

Item Type: Article
Authors :
AuthorsEmailORCID
Nazarpour, KUNSPECIFIEDUNSPECIFIED
Sanei, SUNSPECIFIEDUNSPECIFIED
Shoker, LUNSPECIFIEDUNSPECIFIED
Chambers, JAUNSPECIFIEDUNSPECIFIED
Date : 2006
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:13
Last Modified : 28 Mar 2017 14:13
URI: http://epubs.surrey.ac.uk/id/eprint/742484

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