Classification and recognition of left and right hand motion imagination based on CNN-LSTM

Chen Fan; Yuanyuan Yan; Ming Jing; Xiaodong Zhang; Ye Liu; Junzi Zhang; Xiaoqi Zhang; Fuhua Huang

doi:10.1117/12.2675245

25 May 2023 Classification and recognition of left and right hand motion imagination based on CNN-LSTM

Chen Fan, Yuanyuan Yan, Ming Jing, Xiaodong Zhang, Ye Liu, Junzi Zhang, Xiaoqi Zhang, Fuhua Huang

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126361S (2023) https://doi.org/10.1117/12.2675245
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

Aiming at the problems that the traditional classification and recognition methods of left and right-handed motor imagery EEG signals require prior knowledge and feature extraction requires manual design, the process is cumbersome, and the recognition accuracy is not high, A one-dimensional CNN-LSTM network model that can automatically learn signal features is proposed based on the public motor imagery dataset. The CNN-LSTM network model uses a one-dimensional CNN network to automatically learn and extract the deep-level features of EEG time series, and send the feature sequence to the LSTM classifier for classification. The recognition accuracy of the proposed algorithm is 93.57%. Compared with other algorithms, the proposed algorithm can obtain higher recognition accuracy, and at the same time, it can omit the tedious data preprocessing and feature extraction steps. The proposed method is of great significance to the research on brain-computer interface recognition algorithms.

Citation Download Citation

Chen Fan, Yuanyuan Yan, Ming Jing, Xiaodong Zhang, Ye Liu, Junzi Zhang, Xiaoqi Zhang, and Fuhua Huang "Classification and recognition of left and right hand motion imagination based on CNN-LSTM", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126361S (25 May 2023); https://doi.org/10.1117/12.2675245

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KEYWORDS

Electroencephalography

Education and training

Feature extraction

Detection and tracking algorithms

Data modeling

Machine learning

Signal processing

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