Electroencephalogram-based brain-computer interface (BCI) has potential applications in various fields, but their development is hindered by limited data and significant cross-individual variability. Inspired by the principles of learning and memory in the human hippocampus, we propose a multi-task (MT) classification model, called AM-MTEEG, which combines learning-based impulsive neural representations with bidirectional associative memory (AM) for cross-individual BCI classification tasks. The model treats the EEG classification of each individual as an independent task and facilitates feature sharing across individuals. Our model consists of an impulsive neural population coupled with a convolutional encoder-decoder to extract shared features and a bidirectional associative memory matrix to map features to class. Experimental results in two BCI competition datasets show that our model improves average accuracy compared to state-of-the-art models and reduces performance variance across individuals, and the waveforms reconstructed by the bidirectional associative memory provide interpretability for the model's classification results. The neuronal firing patterns in our model are highly coordinated, similarly to the neural coding of hippocampal neurons, indicating that our model has biological similarities.

翻译：基于脑电图（EEG）的脑机接口（BCI）在多个领域具有潜在应用前景，但其发展受到数据量有限及显著的个体间差异的制约。受人类海马体学习与记忆机制的启发，我们提出一种多任务（MT）分类模型AM-MTEEG，该模型将基于学习的脉冲神经表征与双向关联记忆（AM）相结合，用于跨个体的BCI分类任务。该模型将每个个体的EEG分类视为独立任务，并促进个体间的特征共享。模型由脉冲神经元群与卷积编码器-解码器耦合构成，用于提取共享特征；并通过双向关联记忆矩阵将特征映射至相应类别。在两个BCI竞赛数据集上的实验结果表明，相较于现有先进模型，我们的模型在平均准确率上有所提升，同时降低了不同个体间的性能方差，且双向关联记忆重建的波形为分类结果提供了可解释性。模型中神经元的放电模式呈现高度协调性，类似于海马神经元的神经编码机制，表明该模型具有一定的生物学相似性。