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

Author

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

Author_Institution

Centre of Digital Signal Process., Cardiff Univ., Cardiff, UK

fYear

2006

fDate

4-8 Sept. 2006

Firstpage

1

Lastpage

4

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.

Keywords

brain-computer interfaces; electroencephalography; medical signal processing; signal classification; support vector machines; BCI system; EEG signals; PARAFAC-SVM method; STF decomposition; SVM classifier; brain computer interfacing; electroencephalogram signal; frequency 8 Hz to 13 Hz; hybrid parallel factor analysis-support vector machines; left index imagery movement classification; parallel EEG space-time-frequency decomposition; right index imagery movement classification; spatial distribution; spatial-temporal-spectral characteristics; spectral distribution; temporal distribution; Brain modeling; Electroencephalography; Indexes; Kernel; Scalp; Support vector machines; Time-frequency analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing Conference, 2006 14th European

Conference_Location

Florence

ISSN

2219-5491

Type

conf

Filename

7071225