Multiattention Adaptation Network for Motor Imagery Recognition

Peiyin Chen, Zhongke Gao* (Corresponding Author), Miaomiao Yin, Jialing Wu, Kai Ma, Celso Grebogi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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 the time-consuming calibration procedure. To address these issues, a novel multiattention adaptation network integrating the multiple attention mechanism and transfer learning is proposed to classify the EEG signals. First, the multiattention layer is introduced to automatically capture the dominant brain regions relevant to mental tasks without incorporating any prior knowledge about the physiology. Then, a multiattention 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, and 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-the-art 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.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Early online date28 Oct 2021
DOIs
Publication statusE-pub ahead of print - 28 Oct 2021

Keywords

  • Brain-computer interface (BCI)
  • Deep learning
  • electroencephalogram (EEG)
  • Electroencephalography
  • Feature extraction
  • motor imagery (MI)
  • multiple attentions mechanism
  • Signal resolution
  • Task analysis
  • Training
  • transfer learning
  • Transfer learning

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