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

Apr 15, 2025IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

A Neural Network Model Using Sound Filters, Multi-Path Spatial-Temporal Processing, and Attention for Motor Imagery Brain-Computer Interfaces

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Abstract

The proposed model achieves an average accuracy of 80.21% on a four-class motor imagery dataset.

A deep learning model named Sinc-multibranch-attention network (SMANet) combines Sinc convolution, multibranch spatial-temporal convolutional neural network, and an attention mechanism for motor imagery classification.
Sinc convolution acts as a band-pass filter to enhance data filtering processes.
Pointwise convolution integrates feature information across different frequency ranges, improving feature representation.
The model utilizes a channel attention module to enhance local channel feature extraction and calibrate feature mapping.
SMANet incorporates a multi-objective optimization scheme that combines cross-entropy loss and central loss to enhance discriminative features.
The performance of SMANet surpasses current state-of-the-art methods in decoding spatial-spectral-temporal information from EEG signals.

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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.

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

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