Cross-Group EEG Emotion Recognition Based on Phase Space Reconstruction Topology.

Due to the interference of artifacts and the nonlinearity of electroencephalogram (EEG) signals, the extraction of representational features has become a challenge in EEG emotion recognition. In this work, we reduce the dimensionality of phase space trajectories by introducing local linear embedding (LLE), which projects the trajectories onto a 2-D plane while preserving their local topological structure, and innovatively construct 16 topological features from different perspectives to quantitatively describe the nonlinear dynamic patterns induced by emotions on a multi-scale level. By using independent feature evaluation, we select core features with significant discrimination and combine the activation patterns of brain topography with model gain ranking to optimize the electrode channels. Validation of the SEED and HIED datasets resulted in subject-dependent average accuracies of 90.33% for normal-hearing subjects (3-Class) and 77.17% for hearing-impaired subjects (4-Class), and we also used differential entropy (DE) features to explore the potential of integrating topological features. By quantifying topological features, the 6-Class task achieved an average accuracy of 77.5% in distinguishing emotions across different subject groups.