Journal
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Volume 25, Issue 4, Pages -Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0129065715500136
Keywords
Brain computer interface (BCI); electroencephalography; classification; tensor Factorization
Categories
Funding
- National Key Basic Research Program of China [2015CB856004]
- JSPS KAKENHI [15K15955]
- National Natural Science Foundation of China [91120305, 61272251, 61202155]
- China Scholarship Council (CSC)
- Grants-in-Aid for Scientific Research [15K15955] Funding Source: KAKEN
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Motor imagery-based brain-computer interfaces (BCIs) training has been proved to be an effective communication system between human brain and external devices. A practical problem in BCI-based systems is how to correctly and efficiently identify and extract subject-specific features from the blurred scalp electroencephalography (EEG) and translate those features into device commands in order to control external devices. In real BCI-based applications, we usually define frequency bands and channels configuration that related to brain activities beforehand. However, a steady configuration usually loses effects due to individual variability among different subjects in practical applications. In this study, a robust tensor-based method is proposed for a multiway discriminative subspace extraction from tensor-represented EEG data, which performs well in motor imagery EEG classification without the prior neurophysiologic knowledge like channels configuration and active frequency bands. Motor imagery EEG patterns in spatial-spectral-temporal domain are detected directly from the multidimensional EEG, which may provide insights to the underlying cortical activity patterns. Extensive experiment comparisons have been performed on a benchmark dataset from the famous BCI competition III as well as self-acquired data from healthy subjects and stroke patients. The experimental results demonstrate the superior performance of the proposed method over the contemporary methods.
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