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

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 th...

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Bibliographic Details
Main Authors: Nazarpour, Kianoush, Sanei, Saeid, Shoker, Leor, Chambers, Jonathon A.
Format: Conference Proceeding
Language:English
Subjects:
Brain modeling
Electroencephalography
Indexes
Kernel
Scalp
Support vector machines
Time-frequency analysis
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Summary: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.
ISSN:2219-5491
2219-5491