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

• Published in: Journal of neuroscience methods Vol. 255; pp. 85 - 91
• Main Authors: Zhang, Yu, Zhou, Guoxu, Jin, Jing, Wang, Xingyu, Cichocki, Andrzej
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 30.11.2015
• Subjects: Access to Information; Brain - physiology; Brain-Computer Interfaces; Brain–computer interface (BCI); Common spatial pattern (CSP); Datasets as Topic; Electroencephalogram (EEG); Electroencephalography - methods; Humans; Imagination - physiology; Motor imagery (MI); Psychomotor Performance - physiology; Signal Processing, Computer-Assisted; Sparse regression; Support Vector Machine; Motor imagery (MI); Sparse regression; Electroencephalogram (EEG); Common spatial pattern (CSP); Brain–computer interface (BCI)
• ISSN: 0165-0270; 1872-678X; 1872-678X
• DOI: 10.1016/j.jneumeth.2015.08.004

•This study proposes a sparse filter band common spatial pattern (SFBCSP) for optimizing the spatial patterns.•Experimental results on two public EEG datasets (BCI Competition III dataset IVa and BCI Competition IV IIb) confirm the effectiveness of SFBCSP.•The optimized spatial patterns by SFBCSP give overall better MI classification accuracy in comparison with several competing methods.•Our study suggests that the proposed SFBCSP is a potential method for improving the performance of MI-based BCI. 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. 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. 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. The optimized spatial patterns by SFBCSP give overall better MI classification accuracy in comparison with several competing methods. The proposed SFBCSP is a potential method for improving the performance of MI-based BCI.