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


METHODS
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.


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.
Key Laboratory for Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China. Electronic address: yuzhang@ecust.edu.cn.
Yu
Y
Zhang
Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Wako-shi, Saitama 351-0198, Japan.
Guoxu
G
Zhou
Key Laboratory for Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China.
Jing
J
Jin
Key Laboratory for Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China.
Xingyu
X
Wang
Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Wako-shi, Saitama 351-0198, Japan; System Research Institute, Polish Academy of Sciences, Warsaw 00-901, Poland.
Andrzej
A
Cichocki
D022126:Access to Information; D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D066264:Datasets as Topic; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D007092:Imagination / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine
Brain–computer interface (BCI); Common spatial pattern (CSP); Electroencephalogram (EEG); Motor imagery (MI); Sparse regression