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- W3134729468 abstract "At present, most of the classification studies on EEG signals of motor imagination focus on the classification of left and right hands. In this paper, a feature analysis method based on the combination of brain functional network and common spatial pattern is proposed to solve the dichotomy of unilateral (right) hand motor imagination. Firstly, the phase analysis between Mu rhythm and Beta rhythm signals is carried out, and then the brain functional network topology is drawn according to the phase synchronization information matrix. Through analysis, Mu rhythm of MI - EEG signals than Beta rhythm is more active, and phase synchronization oscillation is concentrated in the center of the brain area C, FC and CP. Then, the concept of graph theory is used to extract the network attribute features under two rhythms: clustering coefficient and shortest path length, and the common space mode method is used to extract the spatial domain features, and the two types of features form an 8-dimensional feature vector. Finally, the features are classified by SVM, and the average classification accuracy is 94.69%, which is higher than that of the two methods. The research content of this paper breaks through the traditional right-handed MI-EEG classification problem, and the method in this paper does not need to consider the ERD/ERS phenomenon, thus expanding the range of data that can be classified." @default.
- W3134729468 created "2021-03-15" @default.
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- W3134729468 date "2020-07-20" @default.
- W3134729468 modified "2023-09-27" @default.
- W3134729468 title "Research on feature extraction method based on brain network and CSP for MI-EEG signals" @default.
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- W3134729468 doi "https://doi.org/10.1109/ssd49366.2020.9364201" @default.
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