Joint multi-layer network and coupling redundancy minimization for semi-supervised EEG-based emotion recognition

Processing high-level cognitive functions like emotion involves dynamic interaction among multiple brain regions. Interactions involving within- and cross-frequency couplings across these regions are paramount in supporting brain functions. Existing emotion recognition models predominantly focus on within-frequency couplings. However, they lack the incorporation of cross-frequency couplings and within-frequency interactions, essential for providing a comprehensive representation of emotional states. To address this limitation, we propose a novel semi-supervised model for emotion recognition that incorporates a multi-layer network and coupling redundancy minimization (JMNCRM) into a unified framework. First, we construct a generalized multi-layer network that embeds rich coupling information about within- and cross-frequency couplings through cosine similarity of features. Then, without increasing the feature dimensionality, the multi-layer network is incorporated into a discriminative linear regression model as a redundant minimum regularization term. During the optimization process, our model selects the most discriminative and non-redundant feature subsets for emotion recognition while retaining the rich structural, discriminative, and coupling information of electroencephalogram (EEG) data in the learned projection subspace. Extensive experimental results on two public datasets and our music-evoked emotion dataset demonstrate that the JMNCRM model outperforms other state-of-the-art algorithms regarding classification performance. Additionally, the intrinsic activation patterns revealed by JMNCRM are consistent with emotional cognition. The code for JMNCRM will be available at https://github.com/czxyhll/JMNCRM.