Learning Regional Attention Convolutional Neural Network for Motion Intention Recognition Based on EEG Data

Learning Regional Attention Convolutional Neural Network for Motion Intention Recognition Based on EEG Data

Zhijie Fang, Weiqun Wang, Shixin Ren, Jiaxing Wang, Weiguo Shi, Xu Liang, Chen-Chen Fan, Zeng-Guang Hou

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 1570-1576. https://doi.org/10.24963/ijcai.2020/218

Recent deep learning-based Brain-Computer Interface (BCI) decoding algorithms mainly focus on spatial-temporal features, while failing to explicitly explore spectral information which is one of the most important cues for BCI. In this paper, we propose a novel regional attention convolutional neural network (RACNN) to take full advantage of spectral-spatial-temporal features for EEG motion intention recognition. Time-frequency based analysis is adopted to reveal spectral-temporal features in terms of neural oscillations of primary sensorimotor. The basic idea of RACNN is to identify the activated area of the primary sensorimotor adaptively. The RACNN aggregates a varied number of spectral-temporal features produced by a backbone convolutional neural network into a compact fixed-length representation. Inspired by the neuroscience findings that functional asymmetry of the cerebral hemisphere, we propose a region biased loss to encourage high attention weights for the most critical regions. Extensive evaluations on two benchmark datasets and real-world BCI dataset show that our approach significantly outperforms previous methods.
Keywords:
Humans and AI: Brain Sciences
Multidisciplinary Topics and Applications: AI for Life Science
Robotics: Human Robot Interaction