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<p>Motor imagery (MI) is a fundamental paradigm in brain-computer interfaces (BCIs), extensively employed to assist individuals with disabilities to operate external devices. Accurate decoding of MI signals is essential for effective interaction. However, robust decoding remains a challenge due to the inherent complexity and variability of MI-EEG signals. To address this issue, we propose an innovative Dual-Branch Multi-Attention Temporal Convolutional Network (DBMATCN) to improve the performance of MI-EEG signal classification. First, the dual-branch structure extracts rich spatial–temporal features. Then, the channel attention enhances local channel feature extraction and calibrate feature mapping. Next, by combining a sliding window technique and multi-head locality self-attention improves the feature representation of MI-EEG signals by emphasizing the most relevant features. Finally, the temporal convolution fusion network decoding module is used to extensively capture comprehensive temporal features from MI data and carry out the classification task. DBMATCN achieves average accuracies of 88.08%, 96.83%, and 89.71% in inter-session validation on the BCI-IV-2a, HGD, and BCI-IV-2b datasets, respectively. In cross-validation, the model reaches an accuracy of 85.14%, and in the subject-independent scenario, it attains 71.78%. DBMATCN outperforms all baseline models in these cases. These results suggest that our model is effective in decoding MI signals.</p>

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A dual-branch neural network and attention mechanism for decoding EEG-based motor imagery

  • 期刊论文

  • 发表日期：

    2025年11月4日

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  • Yangchuang Wang,
  • Hongli Yu,
  • Xiuzhi Zhao,
  • Xiaozhe Yin,
  • Hongxin Li,
  • Chunfang Wang

摘要

Motor imagery (MI) is a fundamental paradigm in brain-computer interfaces (BCIs), extensively employed to assist individuals with disabilities to operate external devices. Accurate decoding of MI signals is essential for effective interaction. However, robust decoding remains a challenge due to the inherent complexity and variability of MI-EEG signals. To address this issue, we propose an innovative Dual-Branch Multi-Attention Temporal Convolutional Network (DBMATCN) to improve the performance of MI-EEG signal classification. First, the dual-branch structure extracts rich spatial–temporal features. Then, the channel attention enhances local channel feature extraction and calibrate feature mapping. Next, by combining a sliding window technique and multi-head locality self-attention improves the feature representation of MI-EEG signals by emphasizing the most relevant features. Finally, the temporal convolution fusion network decoding module is used to extensively capture comprehensive temporal features from MI data and carry out the classification task. DBMATCN achieves average accuracies of 88.08%, 96.83%, and 89.71% in inter-session validation on the BCI-IV-2a, HGD, and BCI-IV-2b datasets, respectively. In cross-validation, the model reaches an accuracy of 85.14%, and in the subject-independent scenario, it attains 71.78%. DBMATCN outperforms all baseline models in these cases. These results suggest that our model is effective in decoding MI signals.