EEG Classification Algorithm of Motor Imagery Based on CNN-Transformer Fusion Network

EasyChair Preprint 8406

Abstract

In recent years, with the development of social economy and technology, the brain-computer interface based on motor imagery(MI-BCI) has gradually become the focus content of many re-searchers. However, the motor imagery EEG signal (MI-EEG) itself has the characteristics of non-linearity and low signal-to-noise ratio, and because the characteristics of different domains of MI-EEG cannot be effectively combined, the recognition rate of MI-EEG is unsatisfactory. To overcome the above problems, this paper proposes a Transformer-based one-dimensional convolutional neural network model(CNN-Transformer) for the classification and recognition of four types of motor imagery EEG signals. Firstly, the artifacts of the original EEG are removed and new time-space-frequency features are constructed by preprocessing such as bandpass filtering and PCA dimensionality reduction; then the local features in the time dimension are extracted through the convolution and pooling operations of 1D-CNN, while reducing the time The dimension of the feature; next, the Transformer based on the attention mechanism is used to extract more abstract and high-level temporal features from multiple perspectives; finally, the classification results are integrated and output through the fully connected layer. The performance of the CNN-Transformer model is evaluated using the competition dataset 2008 BCI-Competition 2A. The results show that the average accuracy and kappa value of the CNN-Transformer model are as high as 99.29%(±0.07%) and 98.43%(±0.21), respectively, which are 3.72% and 7.68% higher than the classical architecture (CNN-LSTM). This model provides a design idea for improving the accuracy of MI-EEG classification and recognition, and also lays a foundation for the wide application of MI-BCI.

Keyphrases: Brain Computer Interface, Convolutional Neural Network, Motor Imagery, classification and recognition, deep learning, transformer

@booklet{EasyChair:8406,
  author    = {Haofeng Liu and Yuefeng Liu and Yue Wang and Bo Liu and Xiang Bao},
  title     = {EEG Classification Algorithm of Motor Imagery Based on CNN-Transformer Fusion Network},
  howpublished = {EasyChair Preprint 8406},
  year      = {EasyChair, 2022}}