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An improved multi-scale convolution and transformer network for EEG-based motor imagery decoding
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DOI：10.1007/s13042-025-02556-6.png)
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Abstract
En 中文
Motor imagery (MI) is currently one of the most researched brain-computer interface (BCI) paradigms, with convolutional neural networks (CNNs) being extensively used for decoding electroencephalogram (EEG) signals. However, numerous studies have demonstrated that the optimal convolution scale varies across subjects and even within different sessions for the same subject. Additionally, EEG signals are time-series data in which temporal dependencies are critical for decoding performance. Consequently, CNN-based models, which are limited by their receptive fields, often struggle to extract highly discriminative features from EEG signals with low signal-to-noise ratios. To address these challenges, an improved multi-scale convolution and Transformer network (IMCTNet) is proposed for decoding MI tasks. In this network, we design a multi-scale temporal convolution block with a channel attention mechanism, enabling adaptive weighting of feature maps across scales. Additionally, we employ Transformer to capture global temporal dependencies, compensating for the limited receptive field of CNNs. The experimental results demonstrate that the IMCTNet outperforms state-of-the-art models, achieving superior classification accuracy (81.83% for Dataset BCI competition IV 2a, 86.47% for Dataset BCI competition IV 2b, and 74.19% for Dataset OpenBMI). The code for IMCTNet is available at https://github.com/ydxswys1/EEG-IMCTNet.
Keywords:
Brain-computer interface (BCI)
Electroencephalography (EEG)
Motor imagery (MI)
Improved multi-scale convolution
Transformer
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