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Decoding Silent Reading EEG Signals Using Adaptive Feature Graph Convolutional Network
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DOI：10.1109/LSP.2023.3337727.png)
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Abstract
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Decoding silent reading Electroencephalography (EEG) signals is challenging because of its low signal-to-noise ratio. In addition, EEG signals are typically non-Euclidean structured, therefore merely using a two-dimensional matrix to represent the variation of sampling points of each channel in time cannot richly represent the spatial connection between channels. Furthermore, due to the individual differences in EEG signals, a fixed representation cannot adequately represent the temporal and spatial associations between channels in real time. In this letter, we use the feature matrix and its adaptive graph structure to represent each EEG signal. Then, we use them as inputs and propose a novel Adaptive Feature Graph Convolutional Network (AFGCN) to decode the silent reading EEG signals. We classify silent reading EEG signals under different tasks of 16 subjects from two publicly available datasets. The experimental results demonstrate that our proposed method achieves higher decoding accuracy than state-of-the-art EEG classification networks on both datasets. Among them, the highest classification accuracy for the four classes is 83.33%. The study could promote the application and development of BCI technology for silent reading EEG signal decoding. It can also provide an efficient and convenient communication method for patients with language impairment.
Keywords:
Electroencephalography
Decoding
Task analysis
Training
Adaptive systems
Symmetric matrices
Convolutional neural networks
Electroencephalography (EEG)
silent reading
graph convolutional network
adaptive graph
language impairment
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