Multiattention Adaptation Network for Motor Imagery Recognition

Brain computer interface (BCI) based on motor imagery Electroencephalogram (EEG) has been widely used in various applications. Despite the previous efforts, the remained major challenges are effective feature extraction and time consuming calibration procedure. To address these issues, a novel Multi-Attention Adaptation Network integrating multiple attentions mechanism and transfer learning is proposed to classify the EEG signals. Firstly, the multi-attention layer is introduced to automatically capture the dominant brain regions relevant to mental tasks without incorporating any prior knowledge about
the physiology. Then, a multi-attention convolutional neural network is employed to extract deep representation from raw EEG signals. Especially, a domain discriminator is applied to deep representation to reduce the differences between sessions for target subjects. The extensive experiments are conducted on three public EEG datasets (Dataset IIa and IIb of BCI Competition IV, High Gamma dataset), achieving the competitive performance with average classification accuracy of 81.48%, 82.54% and 93.97%, respectively. All the results outperform the state-of-theart algorithms demonstrate the effectiveness and robustness of the proposed method. Importantly, we confirm that it is easier and more appropriate to transfer the information from local brain regions than from the whole brain. This enhances the transfer ability of deep features and hence it improves the performance of BCI systems.