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

Kianoush Nazarpour, Saeid Sanei, Leor Shoker, Jonathon A. Chambers

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publication2006 14th European Signal Processing Conference
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-4
Number of pages4
Publication statusPublished - 8 Sept 2006
Event2006 14th European Signal Processing Conference - Florence, Italy
Duration: 4 Sept 20068 Sept 2006

Publication series

Name
PublisherIEEE
ISSN (Print)2219-5491

Conference

Conference2006 14th European Signal Processing Conference
Country/TerritoryItaly
CityFlorence
Period4/09/068/09/06

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