Convolutional recurrent neural network-based EEG signal classification in motor imagery

Qiang Hui; Yuxiang Zhang

doi:10.1117/12.3055974

9 January 2025 Convolutional recurrent neural network-based EEG signal classification in motor imagery

Qiang Hui, Yuxiang Zhang

Author Affiliations +

Proceedings Volume 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024); 134860H (2025) https://doi.org/10.1117/12.3055974
Event: Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 2024, Chengdu, China

Abstract

Brain-computer interface(BCI) is a method of extracting EEG signals by specific means and decoding them using signal processing algorithms to help people with motor disabilities to interact with the outside world through external devices. In order to improve the EEG signal pattern recognition rate of motor imagery. A pattern recognition method of neural network feature fusion combining convolutional neural network (CNN) and recurrent neural network (RNN) serial connections is proposed, and two different RNNs are used for experimental comparison. The proposed method is validated using the BCI Competition IV 2a dataset, and the experimental results show that the proposed method can effectively improve the multi-classification accuracy.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qiang Hui and Yuxiang Zhang "Convolutional recurrent neural network-based EEG signal classification in motor imagery", Proc. SPIE 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 134860H (9 January 2025); https://doi.org/10.1117/12.3055974

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