SMANet: A Model Combining SincNet, Multi-Branch Spatial-Temporal CNN, and Attention Mechanism for Motor Imagery BCI

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 33; pp. 1497 - 1508
• Main Authors: Wang, Danjie, Wei, Qingguo
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2025
• Subjects: Accuracy; Adult; Algorithms; Attention; attention mechanism; Attention mechanisms; Brain modeling; Brain-Computer Interfaces; Convolution; Convolutional neural networks; Decoding; Deep Learning; Electroencephalography; Feature extraction; Frequency-domain analysis; Humans; Imagination - physiology; Male; motor imagery; Motors; Movement; Neural Networks, Computer; Signal Processing, Computer-Assisted; Signal-To-Noise Ratio; SincNet
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2025.3560993

Building a brain-computer interface (BCI) based on motor imagery (MI) requires accurately decoding MI tasks, which poses a significant challenge due to individual discrepancy among subjects and low signal-to-noise ratio of EEG signals. We propose an end-to-end deep learning model, Sinc-multibranch-attention network (SMANet), which combines a SincNet, a multibranch spatial-temporal convolutional neural network (MBSTCNN), and an attention mechanism for MI-BCI classification. Firstly, Sinc convolution is utilized as a band-pass filter bank for data filtering; Second, pointwise convolution facilitates the effective integration of feature information across different frequency ranges, thereby enhancing the overall feature expression capability; Next, the resulting data are fed into the MBSTCNN to learn a deep feature representation. Thereafter, the outputs of the MBSTCNN are concatenated and then passed through an efficient channel attention (ECA) module to enhance local channel feature extraction and calibrate feature mapping. Ultimately, the feature maps yielded by ECA are classified using a fully connected layer. This model SMANet enhances discriminative features through a multi-objective optimization scheme that integrates cross-entropy loss and central loss. The experimental outcomes reveal that our model attains an average accuracy of 80.21% on the four-class MI dataset (BCI Competition IV 2a), 84.02% on the two-class MI dataset (BCI Competition IV 2b), and 72.70% on the two-class MI dataset (OpenBMI). These results are superior to those of the current state-of-the-art methods. The SMANet is capable to effectively decoding the spatial-spectral-temporal information of EEG signals, thereby enhancing the performance of MI-BCIs.