Name
Title | ||
|---|---|---|
PARALLEL SPACE-TIME-FREQUENCY DECOMPOSITION OF EEG SIGNALS FOR BRAIN COMPUTER INTERFACING |
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 consid- ered. Within this novel scheme, we develop a parallel EEG space-time-frequency (STF) decomposition in m band (8-13 Hz) at the preprocessing stage of the BCI system. Using PARAFAC, we elaborate two distinct factors in m band for each EEG trial. SVM classifier is utilised to classify the spa- tial distribution of the movement related factor. This factor is distinguished by its spectral, temporal, and spatial distri- bution. |
Year | Venue | Field |
|---|---|---|
2006 | EUSIPCO | Pattern recognition,Computer science,Brain–computer interface,Interfacing,Speech recognition,Preprocessor,Artificial intelligence,Svm classifier,Space time frequency,Electroencephalography |
DocType | Citations | PageRank |
Conference | 7 | 0.56 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kianoush Nazarpour | 1 | 75 | 19.08 |
| Saeid Sanei | 2 | 530 | 72.63 |
| L. Shoker | 3 | 82 | 7.54 |
| Jonathon Chambers | 4 | 868 | 82.37 |