Motor imagery EEG classification method: based on a novel BiLSTM-Attention-CNN hybrid neural network

Hongxing Chu; Chaozhu Zhang

doi:10.1117/12.2643730

9 October 2022 Motor imagery EEG classification method: based on a novel BiLSTM-Attention-CNN hybrid neural network

Hongxing Chu, Chaozhu Zhang

Proceedings Volume 12246, 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022); 122461B (2022) https://doi.org/10.1117/12.2643730
Event: 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), 2022, Qingdao, China

Abstract

Brain-computer interface (BCI) technology is a human-computer interaction technology that realizes the connection and communication between the human brain and external equipment by collecting and decoding electroencephalograph (EEG) signals. It is a major direction for the development of BCI to analyze and decode the incoming EEG signal in real time through online BCI systems to enable the control of external devices. In order to improve the efficiency of online BCI, it is important to further improve the recognition accuracy and reduce the classification time. Long Short-Term Memory (LSTM) network can improve the recognition accuracy by using the temporal characteristics of EEG signals through its temporal recursive structure. Therefore, the optimized Bi-directional Long Short-Term Memory (BiLSTM) can well express the long-term dependent information in the input. In the study, a new model was proposed, the Attention mechanism was introduced to the BiLSTM while integrating Convolutional Neural Networks (CNN). In the case of avoiding gradient disappearance or gradient explosion, the problem of ignoring correlation information between channels is solved, and learning high-dimensional features in EEG data. This study improves the accuracy of motor image (MI) EEG signal classification under the condition of saving the steps of feature extraction, which is of great significance for the development of online BCI systems.

Citation Download Citation

Hongxing Chu and Chaozhu Zhang "Motor imagery EEG classification method: based on a novel BiLSTM-Attention-CNN hybrid neural network", Proc. SPIE 12246, 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), 122461B (9 October 2022); https://doi.org/10.1117/12.2643730

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KEYWORDS

Electroencephalography

Neural networks

Brain-machine interfaces

Signal processing

Feature extraction

Image classification

Data modeling

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