文献类型：期刊文献

英文题名：Optimizing spatial patterns with sparse filter bands for motor-imagery based brain-computer interface

作者：Zhang, Yu[1];Zhou, Guoxu[2];Jin, Jing[1];Wang, Xingyu[1];Cichocki, Andrzej[2,3]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]RIKEN Brain Sci Inst, Lab Adv Brain Signal Proc, Wako, Saitama 3510198, Japan;[3]Polish Acad Sci, Syst Res Inst, PL-00901 Warsaw, Poland

年份：2015

卷号：255

起止页码：85

外文期刊名：JOURNAL OF NEUROSCIENCE METHODS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000364247600010)】；

基金：This study was supported in part by the Nation Nature Science Foundation of China under Grant 61305028, Grant 91420302, Grant 61573142, Grant 61203127, Grant 61201124, Fundamental Research Funds for the Central Universities under Grant WH1314023, Grant WG1414005, Grant WH1414022, the Guangdong Natural Science Foundation under Grant 2014A030308009, and JSPS KAKENHI Grant 26730125.

语种：英文

外文关键词：Brain-computer interface (BCI); Common spatial pattern (CSP); Electroencephalogram (EEG); Motor imagery (MI); Sparse regression

摘要：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 lib) 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. (C) 2015 Elsevier B.V. All rights reserved.