Journal
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Volume 27, Issue 2, Pages -Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0129065716500325
Keywords
Brain-computer interface; common spatial pattern; electroencephalogram; frequency band; motor imagery; sparse Bayesian learning
Categories
Funding
- National Natural Science Foundation of China [61305028, 91420302, 61573142]
- Fundamental Research Funds for the Central Universities [WH1314023, WG1414005, WH1516018, WH1414022]
- Chenguang Program - Shanghai Education Development Foundation [14CG31]
- Shanghai Municipal Education Commission
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Effective common spatial pattern (CSP) feature extraction for motor imagery (MI) electroencephalogram (EEG) recordings usually depends on the filter band selection to a large extent. Subband optimization has been suggested to enhance classification accuracy of MI. Accordingly, this study introduces a new method that implements sparse Bayesian learning of frequency bands (named SBLFB) from EEG for MI classification. CSP features are extracted on a set of signals that are generated by a filter bank with multiple overlapping subbands from raw EEG data. Sparse Bayesian learning is then exploited to implement selection of significant features with a linear discriminant criterion for classification. The effectiveness of SBLFB is demonstrated on the BCI Competition IV IIb dataset, in comparison with several other competing methods. Experimental results indicate that the SBLFB method is promising for development of an effective classifier to improve MI classification.
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