AdaLMNN: Adaptive Large Margin Nearest Neighbor Metric Learning for EEG-Based Emotion Recognition

Electroencephalography (EEG)-based emotion recognition provides an important physiological basis for affective brain–computer interfaces. However, high-dimensional EEG features vary across trials, sessions, and subjects. Euclidean distance and conventional source-only LMNN cannot jointly learn a discriminative geometry and align cross-domain emotion distributions. To address this problem, we propose Adaptive Large Margin Nearest Neighbor (AdaLMNN), an unsupervised domain-adaptation framework for EEG emotion recognition. AdaLMNN learns a shared Mahalanobis projection through normalized large-margin discrimination, adaptive margins, and refreshed source neighborhoods. It further uses confidence-guided target pseudo-labels, class-balanced conditional alignment, and scale regularization with scheduled optimization to reduce source–target mismatch without using target ground-truth labels. We evaluated AdaLMNN on synthetic data and the SEED and SEED-IV datasets using SVM, KNN, and MLP under three protocols. AdaLMNN substantially improved EEG emotion-classification performance, particularly under within-session and session-level shifts. Although cross-subject gains remained dataset- and classifier-dependent, the results demonstrate the potential of AdaLMNN as an interpretable and extensible representation-learning module for EEG emotion recognition under heterogeneous recording conditions.