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Trend-Aware Multiscale Spatial-Temporal Graph Convolution Network for P300 Detection
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DOI：10.1109/TCSS.2025.3645356.png)
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Abstract
En 中文
P300-based brain–computer interfaces (BCIs) enable direct communication between the brain and external devices by decoding P300 potentials, playing a vital role in rehabilitation and cognitive neuroscience research. Accurate detection of P300 potentials is essential for the successful implementation of P300-based BCIs. Current mainstream P300 detection algorithms are based on multichannel electroencephalogram (EEG) signals, and while promising detection results have been achieved, they still suffer from the following issues: 1) insufficient exploitation of the distinct and characteristic overall temporal trend of P300 potentials; and 2) oversimplified aggregation of multichannel EEG information without effectively utilizing the non-Euclidean topological relationships between EEG channels. To address the above issues, we propose a trend-aware multiscale spatial-temporal graph convolutional neural network (TMSGCN) for P300 detection. Specifically, to effectively capture the long-term temporal trend of P300 potentials to improve detection robustness, we explicitly extract the trend component from raw EEG signals along time dimension and utilize it to assist the identification of P300 potentials. Subsequently, a multiscale temporal convolution module (MTCM) is applied to extract multitime scale amplitude features from the processed EEG signals, serving as input for an adaptive graph convolution module (AGCM). The AGCM effectively models the intricate inter-channel relationships as a graph by complementarily considering the structural and functional connectivity of human brain, thereby capturing more discriminative and informative spatial features related to P300 potentials. Extensive experiments on three datasets demonstrate the superiority of TMSGCN over other state-of-the-art P300 detection methods. Furthermore, the results of ablation study and visualization experiments indicate the effectiveness of each component in TMSGCN.
Keywords:
Brain–computer interface (BCI)
electroencephalography (EEG)
graph convolutional network
P300
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