Optimizing spatial patterns with sparse filter bands for motor-imagery based brain-computer interface

Yu Zhang, Guoxu Zhou, Jing Jin, Xingyu Wang, Andrzej Cichocki

Research output: Contribution to journalArticlepeer-review

193 Citations (Scopus)


Background: Common spatial pattern (CSP) has been most popularly applied to motor-imagery (MI) feature extraction for classification in brain-computer interface (BCI) application. Successful application of CSP depends on the filter band selection to a large degree. However, the most proper band is typically subject-specific and can hardly be determined manually. New method: This study proposes a sparse filter band common spatial pattern (SFBCSP) for optimizing the spatial patterns. SFBCSP estimates CSP features on multiple signals that are filtered from raw EEG data at a set of overlapping bands. The filter bands that result in significant CSP features are then selected in a supervised way by exploiting sparse regression. A support vector machine (SVM) is implemented on the selected features for MI classification. Results: Two public EEG datasets (BCI Competition III dataset IVa and BCI Competition IV IIb) are used to validate the proposed SFBCSP method. Experimental results demonstrate that SFBCSP help improve the classification performance of MI. Comparison with existing methods: The optimized spatial patterns by SFBCSP give overall better MI classification accuracy in comparison with several competing methods. Conclusions: The proposed SFBCSP is a potential method for improving the performance of MI-based BCI.

Original languageEnglish
Pages (from-to)85-91
Number of pages7
JournalJournal of Neuroscience Methods
Publication statusPublished - 30 Nov 2015
Externally publishedYes


  • Brain-computer interface (BCI)
  • Common spatial pattern (CSP)
  • Electroencephalogram (EEG)
  • Motor imagery (MI)
  • Sparse regression


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