EEG-based intention recognition with deep recurrent-convolution neural network: Performance and channel selection by Grad-CAM

• Published in: Neurocomputing (Amsterdam) 2020-11, Vol.415, p.225-233
• Main Authors: Li, Yurong, Yang, Hao, Li, Jixiang, Chen, Dongyi, Du, Min
• Format: Article
• Language: English
• Subjects: Channel selection; Deep recurrent-convolution neural network; EEG; Grad-CAM; Intention recognition
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2020.07.072

Electroencephalography (EEG) based Brain-Computer Interface (BCI) enables subjects to communicate with the outside world or control equipment using brain signals without passing through muscles and nerves. Many researchers in recent years have studied the non-invasive BCI systems. However, the efficiency of the intention decoding algorithm is affected by the random non-stationary and low signal-to-noise ratio characteristics of the EEG signal. Furthermore, channel selection is another important issue in BCI systems intention recognition. During intention recognition in BCI systems, the unnecessary information produced by redundant electrodes affects the decoding rate and deplete system resources. In this paper, we introduce a recurrent-convolution neural network model for intention recognition by learning decomposed spatio-temporal representations. We apply the novel Gradient-Class Activation Mapping (Grad-CAM) visualization technology to the channel selection. Grad-CAM uses the gradient of any classification, flowing into the last convolutional layer to produce a coarse localization map. Since the pixels of the localization map correspond to the spatial regions where the electrodes are placed, we select the channels that are more important for decision-making. We conduct an experiment using the public motor imagery EEG dataset EEGMMIDB. The experimental results demonstrate that our method achieves an accuracy of 97.36% at the full channel, outperforming many state-of-the-art models and baseline models. Although the decoding rate of our model is the same as the best model compared, our model has fewer parameters with faster training time. After the channel selection, our model maintains the intention decoding performance of 92.31% while reducing the number of channels by nearly half and saving system resources. Our method achieves an optimal trade-off between performance and the number of electrode channels for EEG intention decoding.