EEG-Based Motor Imagery Classification Via Multi-Scale Convolutional Attention-Guided Capsule Network

Motor imagery (MI)-based brain-computer interfaces (BCIs) rely on accurate decoding of electroencephalography (EEG) signals. However, the non-stationary nature of EEG signals and the complex relationships among discriminative patterns make MI classification a challenging task. To address these issues, this paper proposes a Multi-Scale Convolutional Attention-Guided Capsule Network (MSC-AG-CapsNet) for MI-EEG classification. The proposed framework consists of a multi-scale convolutional module and an attention-guided capsule network module. Specifically, the multi-scale convolutional module employs parallel temporal convolutions with different receptive fields to capture EEG representations at multiple temporal scales. Subsequently, the extracted feature sequence is transformed into primary capsules and aggregated through an attention-guided capsule aggregation mechanism, which replaces conventional dynamic routing with a feed-forward aggregation strategy. In this way, informative relationships among capsule representations can be effectively exploited while avoiding iterative routing operations. Extensive experiments conducted on the BCI Competition IV-2a and IV-2b datasets demonstrate the effectiveness of the proposed framework. Ablation studies further verify the contributions of both the multi-scale convolutional module and the attention-guided capsule aggregation mechanism. The results indicate that MSC-AG-CapsNet provides a competitive and efficient solution for MI-EEG decoding. The code is available at https://github.com/wiaobang/MSC-AG-Capsnet.