GMCDA: Graph-Based Multisource Conditional Distribution Domain Adaptation Network for EEG Recognition of Emotions

Abstract

Electroencephalography (EEG) emotion recognition is critical in human-computer interface applications. EEG is a prevalent tool for discerning various emotional states. However, EEG is confronted with the challenges, including nonstationarity, small amplitude, low signal-to-noise ratio, and significant inter-subject variability. Furthermore, existing methods primarily focus on frequency domain features of EEG signal, overlooking the interchannel relationships. To solve these problems, we introduce a novel approach for emotion recognition using a graph-based conditional distribution-aligned domain adaptation (DA) network. This approach harmonizes both domain-invariant and domain-specific features. With the premise that diverse EEG data share consistent underlying features, we employ dynamic graph convolution to extract the domain-invariant features. Subsequently, we build separate branches for distinct source domains to harness domain-specific features, execute conditional distribution alignment, and eventually determine the scores based on distribution distance. Specifically, the contribution of each source domain is weighted according to its distribution similarity with the target domain, thereby assigning greater importance to more relevant sources. The final outcome is obtained via these weighted scores. The final outcome is ascertained via the associated weighted scores. Based on the leave-one-out cross-validation, we tested our model on the open-source SEED and SEED-IV datasets. The mean accuracy rates were 90.33% and 62.83% in the cross-subject situation, as well as 93.26% and 67.06% in the cross-session situation. Compared with multiple state-of-the-art DA algorithms, our proposed algorithm garners better classification results.