标题： ISAM-MTL：具有可识别尖峰和关联记忆网络的跨被试多任务学习模型 显示英文标题

标题： ISAM-MTL: Cross-subject multi-task learning model with identifiable spikes and associative memory networks

摘要： 脑电图（EEG）的跨被试变异性会降低当前深度学习模型的性能，限制了脑机接口（BCI）的发展。 本文提出了ISAM-MTL，这是一个基于可识别脉冲（IS）表示和关联记忆（AM）网络的多任务学习（MTL） EEG分类模型。 所提出的模型将每个被试的EEG分类视为一个独立任务，并利用跨被试数据训练来促进被试间的特征共享。 ISAM-MTL包含一个脉冲特征提取器，用于捕捉跨被试的共享特征，以及一个被试特定的双向关联记忆网络，该网络通过赫布学习进行训练，以实现高效的和快速的被试内EEG分类。 ISAM-MTL将学习到的脉冲神经表示与双向关联记忆相结合，用于跨被试的EEG分类。 该模型采用标签引导的变分推理来构建可识别的脉冲表示，提高分类准确性。 在两个BCI竞赛数据集上的实验结果表明，ISAM-MTL提高了跨被试EEG分类的平均准确率，同时减少了被试间的性能差异。 该模型进一步表现出少样本学习和EEG下的可识别神经活动特性，使得BCI系统的快速且可解释的校准成为可能。 显示英文摘要

摘要： Cross-subject variability in EEG degrades performance of current deep learning models, limiting the development of brain-computer interface (BCI). This paper proposes ISAM-MTL, which is a multi-task learning (MTL) EEG classification model based on identifiable spiking (IS) representations and associative memory (AM) networks. The proposed model treats EEG classification of each subject as an independent task and leverages cross-subject data training to facilitate feature sharing across subjects. ISAM-MTL consists of a spiking feature extractor that captures shared features across subjects and a subject-specific bidirectional associative memory network that is trained by Hebbian learning for efficient and fast within-subject EEG classification. ISAM-MTL integrates learned spiking neural representations with bidirectional associative memory for cross-subject EEG classification. The model employs label-guided variational inference to construct identifiable spike representations, enhancing classification accuracy. Experimental results on two BCI Competition datasets demonstrate that ISAM-MTL improves the average accuracy of cross-subject EEG classification while reducing performance variability among subjects. The model further exhibits the characteristics of few-shot learning and identifiable neural activity beneath EEG, enabling rapid and interpretable calibration for BCI systems.