Cross-Subject Domain Adaptation for EEG-Based Auditory Attention Decoding via Prototypical Representation

Auditory attention represents a key neural process in the brain's handling of auditory information, essential for effective human communication. Electroencephalography (EEG) has proven to be effective in auditory attention decoding (AAD). However, current methods primarily concentrate on individual subject settings, which fail to achieve robust models across subjects due to notable variations between subjects and noisy labels. To overcome these issues, this paper investigates a novel domain adaptation method using prototypical representation(DApro) to extract both specific and invariant representations across subjects. Specifically, we align the features from the source and target domains to a shared feature space, while considering the relationships with prototypical representations both within and between classes. Features of the same category are clustered together, while those of different categories are separated. The proposed method was evaluated on the publicly available AHU dataset using a leave-one-subject-out cross-validation protocol. The experimental results demonstrate the effectiveness and the superiority of our model in terms of generalization and robustness in the cross-subject auditory attention classification using EEG signals.