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

TL;DR

ISAM-MTL is a novel multi-task learning model for EEG classification that leverages identifiable spike representations and associative memory networks to improve cross-subject accuracy and interpretability in brain-computer interfaces.

Contribution

The paper introduces a new cross-subject EEG classification model combining identifiable spiking features with associative memory networks, enabling rapid, accurate, and interpretable BCI calibration.

Findings

Improves cross-subject EEG classification accuracy

Reduces variability among subjects

Enables rapid calibration with few-shot learning

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.