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

• URL: http://arxiv.org/abs/2501.18089v1
• Date: Thu, 30 Jan 2025 02:00:48 GMT
• Title: ISAM-MTL: Cross-subject multi-task learning model with identifiable spikes and associative memory networks
• Authors: Junyan Li, Bin Hu, Zhi-Hong Guan,
• Abstract summary: Cross-subject variability in EEG degrades performance of current deep learning models. 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.
• Score: 6.240145569484483
• License:
• Abstract: 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.

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