文献类型：期刊文献

英文题名：Sparse Bayesian Learning for Obtaining Sparsity of EEG Frequency Bands Based Feature Vectors in Motor Imagery Classification

作者：Zhang, Yu[1];Wang, Yu[2];Jin, Jing[1];Wang, Xingyu[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Shanghai, Peoples R China;[2]Shanghai Ruanzhong Informat Technol Co Ltd, Shanghai, Peoples R China

年份：2017

卷号：27

期号：2

外文期刊名：INTERNATIONAL JOURNAL OF NEURAL SYSTEMS

收录：;EI(收录号：20162902593448);WOS:【SCI-EXPANDED(收录号:WOS:000391943500002)】；

基金：This study was supported in part by the National Natural Science Foundation of China under Grant 61305028, Grant 91420302, Grant 61573142, Fundamental Research Funds for the Central Universities under Grant WH1314023, Grant WG1414005, WH1516018 and Grant WH1414022, Chenguang Program No. 14CG31 supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission.

语种：英文

外文关键词：Brain-computer interface; common spatial pattern; electroencephalogram; frequency band; motor imagery; sparse Bayesian learning

摘要：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.