Journal
COGNITIVE NEURODYNAMICS
Volume 17, Issue 5, Pages 1283-1296Publisher
SPRINGER
DOI: 10.1007/s11571-022-09892-1
Keywords
Brain-computer interface(BCI); Motor imagery(MI); Graph convolutional neural network (GCN); Channel selection
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Multi-channel EEG is used to capture features for motor imagery based BCI. Removing irrelevant channels can improve classification performance. This study introduces a new method based on graph convolutional neural network for channel selection, achieving significant improvements in performance on three MI datasets.
Multi-channel electroencephalography (EEG) is used to capture features associated with motor imagery (MI) based brain-computer interface (BCI) with a wide spatial coverage across the scalp. However, redundant EEG channels are not conducive to improving BCI performance. Therefore, removing irrelevant channels can help improve the classification performance of BCI systems. We present a new method for identifying relevant EEG channels. Our method is based on the assumption that useful channels share related information and that this can be measured by inter-channel connectivity. Specifically, we treat all candidate EEG channels as a graph and define channel selection as the problem of node classification on a graph. Then we design a graph convolutional neural network (GCN) model for channels classification. Channels are selected based on the outputs of our GCN model. We evaluate our proposed GCN-based channel selection (GCN-CS) method on three MI datasets. On three datasets, GCN-CS achieves performance improvements by reducing the number of channels. Specifically, we achieve classification accuracies of 79.76% on Dataset 1, 89.14% on Dataset 2 and 87.96% on Dataset 3, which outperform competing methods significantly.
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