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Research on classification of motor imagery EEG signals based on TQWT-CSP

Published: 30 March 2023 Publication History

Abstract

In this paper, a novel feature extraction method called TQWT-CSP based on sliding time window is proposed, which can extract the spatial features of multiple time-frequency blocks, so as to select different optimal feature sets for different individuals before motor imagery classification is completed. Firstly, the linear discriminant criterion is used to select channels of the signal after preprocessing; Secondly, the signal is decomposed and reconstructed into multiple subbands by using TQWT, and each subband is divided into 11 time periods which are overlap by using the sliding time window, so as to get several time-frequency blocks; Next, the CSP algorithm is used to obtain the feature set; Finally, the random forest feature selection algorithm is used to select the optimal feature subset and support vector machine is used for classification. In the experiment, 10 subjects in the of GigaDB Motor Dataset are used to verify the performance of the proposed method. The average accuracy, kappa coefficient, sensitivity and specificity using 5-fold cross validation were 82.14%, 64.11%, 80.60% and 83.71% respectively. The results of experiment show that the proposed method improve the classification performance and is at a high level among the numerical measurement indicators given in the state-of- the-art research literature.

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    CSAI '22: Proceedings of the 2022 6th International Conference on Computer Science and Artificial Intelligence
    December 2022
    341 pages
    ISBN:9781450397773
    DOI:10.1145/3577530
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 30 March 2023

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