Single-Channel EEG Classification of Human Attention with Two-Branch Multiscale CNN and Transformer Model

Human attention is one of the important indicators of human normal activity, and the classification of EEG signals helps determining the level of human attention. However, most existing EEG signal classification algorithms based on deep learning networks suffer from the difficulty in parallelization and low efficiency. In this paper, a new model of EEG signal classification is proposed including feature extraction, temporal feature encoding and classification modules. The feature extraction module adopts the two-branch multiscale CNN model and combines the G-bneck module, which improves the efficiency of the model by generating more important characteristics through the channel attention mechanism. In temporal feature encoding module, the Transformer encoder is utilized with parallel computing ability to extract temporal features, and the causal convolution-based position coding method is presented to help solve the translation homogeneity problem. Our algorithm has been tested with intrasubjects and inter-subjects on both self-built and public datasets, and the experimental results prove that the new model achieves competitive classification performance. Our model is used with a single-channel EEG signal acquisition device for human attention, and is also applicable to multi-channel EEG signals.