文献类型：期刊文献

英文题名：Temporally Constrained Sparse Group Spatial Patterns for Motor Imagery BCI

作者：Zhang, Yu[1];Nam, Chang S.[2];Zhou, Guoxu[3];Jin, Jing[4];Wang, Xingyu[4];Cichocki, Andrzej[5,6]

机构：[1]Stanford Univ, Dept Psychiat & Behav Sci, Stanford, CA 94305 USA;[2]North Carolina State Univ, Edward P Fitts Dept Ind & Syst Engn, Raleigh, NC 27695 USA;[3]Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China;[4]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[5]Skolkowo Inst Sci & Technol, Moscow 143025, Russia;[6]RIKEN Brain Sci Inst, Lab Adv Brain Signal Proc, Wako, Saitama 3510198, Japan

年份：2019

卷号：49

期号：9

起止页码：3322

外文期刊名：IEEE TRANSACTIONS ON CYBERNETICS

收录：;EI(收录号：20182505346414);WOS:【SCI-EXPANDED(收录号:WOS:000470988800009)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 91420302 and Grant 61573142, in part by the Fundamental Research Funds for the Central Universities under Grant WH1516018 and Grant 222201717006, in part by the USA National Science Foundation under Grant IIS-1421948 and Grant BCS-1551688, in part by the Ministry of Education and Science of the Russian Federation under Grant 14.756.31.0001, and in part by the Polish National Science Center under Grant 2016/20/W/N24/00354. This paper was recommended by Associate Editor C.-T. Lin. (Corresponding author: Yu Zhang.)

语种：英文

外文关键词：Brain-computer interface (BCI); electroencephalogram (EEG); motor imagery (MI); sparse group spatial pattern; temporal constraint

摘要：Common spatial pattern (CSP)-based spatial filtering has been most popularly applied to electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain-computer interface (BCI) application. The effectiveness of CSP is highly affected by the frequency band and time window of EEG segments. Although numerous algorithms have been designed to optimize the spectral bands of CSP, most of them selected the time window in a heuristic way. This is likely to result in a suboptimal feature extraction since the time period when the brain responses to the mental tasks occurs may not be accurately detected. In this paper, we propose a novel algorithm, namely temporally constrained sparse group spatial pattern (TSGSP), for the simultaneous optimization of filter bands and time window within CSP to further boost classification accuracy of MI EEG. Specifically, spectrum-specific signals are first derived by bandpass filtering from raw EEG data at a set of overlapping filter bands. Each of the spectrum-specific signals is further segmented into multiple subseries using sliding window approach. We then devise a joint sparse optimization of filter bands and time windows with temporal smoothness constraint to extract robust CSP features under a multitask learning framework. A linear support vector machine classifier is trained on the optimized EEG features to accurately identify the MI tasks. An experimental study is implemented on three public EEG datasets (BCI Competition III dataset IIIa, BCI Competition IV datasets IIa, and BCI Competition IV dataset IIb) to validate the effectiveness of TSGSP in comparison to several other competing methods. Superior classification performance (averaged accuracies are 88.5%, 83.3%, and 84.3% for the three datasets, respectively) based on the experimental results confirms that the proposed algorithm is a promising candidate for performance improvement of MI-based BCIs.