AdamGraph: Adaptive Attention-Modulated Graph Network for EEG Emotion Recognition

Abstract

The underlying time-variant and subject-specific brain dynamics lead to inconsistent distributions in electroencephalogram (EEG) topology and representations within and between individuals. However, current works primarily align the distributions of EEG representations, overlooking the topology variability in capturing the dependencies between channels, which may limit the performance of EEG emotion recognition. To tackle this issue, this article proposes an adaptive attention-modulated graph network (AdamGraph) to enhance the subject adaptability of EEG emotion recognition against connection variability and representation variability. Specifically, an attention-modulated graph connection module is proposed to explicitly capture the individual important relationships among channels adaptively. Through modulating the attention matrix of individual functional connections using spatial connections based on prior knowledge, the attention-modulated weights can be learned to construct individual connections adaptively, thereby mitigating individual differences. Besides, a deep node-graph representation learning module is designed to extract long-range interaction characteristics among channels and alleviate the over-smoothing problem of representations. Furthermore, a graph domain co-regularized learning module is imposed to tackle the individual distribution discrepancies in connection and representations across different domains. Extensive experiments on three public EEG emotion datasets, i.e., SEED, DREAMER, and MPED, validate the superior performance of AdamGraph compared with state-of-the-art methods.