Motor Imagery EEG Signal Recognition Based on ACVAE and CNN-LSTM - Hu, Cunlin; Ye, Ye; Li, Jian; Wang, Hongliang; Zhou, Tao; Xie, Nenggang (2024 International Conference on Electronic Engineering and Information Systems (EEISS), 2024)
Hybrid neural networks are able to capture the time-dependency of electroencephalography (EEG) signals, and can therefore effectively perform pattern recognition of motor imagery EEG signals. However, a sufficiently large amount of training data is required to achieve optimal results, so it is necessary to use data augmentation methods to increase the amount of data. Therefore, we propose a data augmentation method based on the Attention Convolutional Variation Autoencoder (ACVAE) and design a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) network for pattern recognition. The BCI Competition IV dataset 2a is used for experimental validation. The results show that the ACVAE method produces higher quality data, with the highest recognition accuracy of 97.16% for a single subject in the four-classification task. Compared to other methods, we proposed method shows excellent performance.