Abstract

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Title:: Multi view deep forest-based decoding algorithm for motor imagery EEG

Author(s):: ZHENG Longxin1; MIAO Minmin1; 2; XU Baoguo3; HU Wenjun1; 2; 1. School of Information Engineering, Huzhou University, Huzhou 313000, China2.Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, China3.School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China

Keywords:: Keywords: motor imagery EEG signal decoding algorithm multi view feature extraction deep forest

Abstract:: Abstract: A decoding algorithm based on multi view deep forest is proposed for solving the problems of complicated feature extraction and low decoding accuracy of motor imagery EEG signal. The energy feature in spatial-temporal-frequency domains which is generated by fine-grained analysis using subband filtering and time window division are processed with sparse selection and temporal scanning for obtaining important shallow energy features and multi instance a priori category features to construct a multi view feature set. Then the hierarchical feature transformation of cascaded forests is used to mine deep level abstract features for EEG coding.The algorithm is tested on two BCI competition datasets and a self-collected dataset, and it is compared with single view feature models, traditional CSP methods and deep neural network algorithms. The proposed method achieves the highest average classification accuracy (91.4%, 75.2% and 70.7%, respectively) in 2003 BCI competition dataset Ⅲ, 2008 BCI competition dataset 2b and self-collected dataset, which suggests that the decoding algorithm based on multi view deep forest has better classification performance.