A Spatio-Temporal Method for Extracting Gamma-Band Features to Enhance Classification in a Rapid Serial Visual Presentation Task

作     者：Xie, Ping Hao, Shencai Zhao, Jing Liang, Zhenhu Li, Xiaoli 

作者机构：Yanshan Univ Sch Elect Engn Key Lab Intelligent Rehabil & Neromodulat Hebei P Qinhuangdao 066004 Hebei Peoples R China Beijing Normal Univ State Key Lab Cognit Neurosci & Learning Beijing 100875 Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF NEURAL SYSTEMS》 (国际神经系统杂志)

年 卷 期：2022年第32卷第3期

页      面：2250010-2250010页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 071003[理学-生理学] 

基　　金：National Natural Science Foundation of China Natural Science Foundation of Hebei Province [F2020203070] 

主　　题：Brain-computer interface electroencephalogram rapid serial visual presentation gamma-band feature 

摘      要：Rapid serial visual presentation (RSVP) is a type of electroencephalogram (EEG) pattern commonly used for target recognition. Besides delta- and theta-band responses already used for classification, RSVP task also evokes gamma-band responses having low amplitude and large individual difference. This paper proposes a filter bank spatio-temporal component, analysis (FBSCA) method, extracting spatio-temporal features of the gamma-band responses for the first time, to enhance the RSVP classification performance. Considering the individual difference in time latency and responsive frequency, the proposed FBSCA method decomposes the gamma-band EEC data into sub-components in different time-frequency-space domains and seeks the weight coefficients to optimize the combinations of electrodes, common spatial pattern (CSP) components, time windows and frequency bands. Two state-of-the-art methods, i.e. hierarchical discriminant principal component analysis (HDPCA) and discriminative canonical pattern matching (DCPM), were used for comparison. The performance was evaluated in 10 x 10 cross validations using a public dataset. Study results showed that the FBSCA method outperformed the other methods regardless of number of training trials. These results suggest that the proposed FBSCA method can enhance the RSVP classification.