A learnable continuous wavelet-based multi-branch attentive convolutional neural network for spatio-spectral-temporal EEG signal decoding

Deep learning methods, particularly convolutional neural networks (CNNs), have advanced the field of motor imagery (MI) by effectively extracting spatio-spectral-temporal (SST) features from electroencephalography (EEG) signals. Image-based CNNs using time-frequency analysis such as wavelet transform have outperformed signal-based CNNs in MI -EEG classification. However, the spatial characteristics of EEG signals can be neglected due to the high dimensionality of spectral-temporal feature space. Moreover, the performance hinges on whether the predefined transformation can capture the class-discriminative spectral-temporal representations of EEG signals well. Despite attempts to alleviate these challenges by selecting EEG electrodes or restricting spectral-temporal feature space, large inter-subject variabilities have impeded successful MIEEG decoding. In this paper, we propose a learnable continuous wavelet-based multi-branch attentive CNN framework for decoding MI -EEG signals. Our method is designed to automatically generate class-discriminative SST representations of EEG signals within subject-specific sub-bands by utilizing learnable wavelet-based convolutions. Additionally, it employs multi-branch attentive CNNs for the effective and efficient extraction of local and global SST features. Our comprehensive evaluation on two public datasets demonstrates that the proposed method significantly outperformed state -of -the -art methods. We also conduct an ablation study under various configuration settings within the proposed framework to demonstrate the effectiveness of our method. The proposed method provides a powerful and innovative approach to MI -EEG signal decoding, with implications for personalized EEG decoding.