Li Junyan, Hu Bin, Guan Zhi-Hong
School of Future Technology, South China University of Technology, Guangzhou, China.
Guangdong Artificial Intelligence and Digital Economy Laboratory, Guangzhou, China.
Front Neurosci. 2025 Mar 6;19:1557287. doi: 10.3389/fnins.2025.1557287. eCollection 2025.
Electroencephalogram-based brain-computer interfaces (BCIs) hold promise for healthcare applications but are hindered by cross-subject variability and limited data. This article proposes a multi-task (MT) classification model, AM-MTEEG, which integrates deep learning-based convolutional and impulsive networks with bidirectional associative memory (AM) for cross-subject EEG classification. AM-MTEEG deals with the EEG classification of each subject as an independent task and utilizes common features across subjects. The model is built with a convolutional encoder-decoder and a population of impulsive neurons to extract shared features across subjects, as well as a Hebbian-learned bidirectional associative memory matrix to classify EEG within one subject. Experimental results on two BCI competition datasets demonstrate that AM-MTEEG improves average accuracy over state-of-the-art methods and reduces performance variance across subjects. Visualization of neuronal impulses in the bidirectional associative memory network reveal a precise mapping between hidden-layer neuron activities and specific movements. Given four motor imagery categories, the reconstructed waveforms resemble the real event-related potentials, highlighting the biological interpretability of the model beyond classification.
基于脑电图的脑机接口（BCI）在医疗保健应用方面具有潜力，但受到跨受试者变异性和有限数据的阻碍。本文提出了一种多任务（MT）分类模型AM-MTEEG，该模型将基于深度学习的卷积和脉冲网络与双向联想记忆（AM）相结合，用于跨受试者脑电图分类。AM-MTEEG将每个受试者的脑电图分类视为一个独立任务，并利用跨受试者的共同特征。该模型由一个卷积编码器-解码器和一群脉冲神经元构建而成，用于提取跨受试者的共享特征，以及一个通过赫布学习的双向联想记忆矩阵，用于对一个受试者内的脑电图进行分类。在两个BCI竞赛数据集上的实验结果表明，AM-MTEEG比现有方法提高了平均准确率，并降低了跨受试者的性能差异。双向联想记忆网络中神经元脉冲的可视化揭示了隐藏层神经元活动与特定运动之间的精确映射。给定四个运动想象类别，重建的波形类似于真实的事件相关电位，突出了该模型在分类之外的生物学可解释性。
