Science and Technology Production
Book Chapter
Authorship
Qibin Zhao
;
CAIAFA, CESAR FEDERICO
;
Andrzej Cichocki
;
and Anh Huy Phan
Date
2009
Publishing House and Editing Place
Springer
Book
Neural Information Processing
(pp. 221-228)
Springer
Springer
ISBN
978-3-642-10676-7
Summary
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In this paper we apply a novel tensor decomposition model of SOD (slice oriented decomposition) to extract slice features from the multichannel time-frequency representation of EEG signals measured for MI (motor imagery) tasks in application to BCI (brain computer interface). The advantages of the SOD based feature extraction approach lie in its capability to obtain slice matrix components across the space, time and frequency domains and the discriminative features across different classes with...
In this paper we apply a novel tensor decomposition model of SOD (slice oriented decomposition) to extract slice features from the multichannel time-frequency representation of EEG signals measured for MI (motor imagery) tasks in application to BCI (brain computer interface). The advantages of the SOD based feature extraction approach lie in its capability to obtain slice matrix components across the space, time and frequency domains and the discriminative features across different classes without any prior knowledge of the discriminative frequency bands. Furthermore, the combination of horizontal, lateral and frontal slice features makes our method more robust for the outlier problem. The experiment results demonstrate the effectiveness of our method.
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Key Words
BCITENSOR DECOMPOSITIONEEG