EEG Emotion Recognition

EEG emotion recognition infers emotional categories, such as valence and arousal, from scalp electrical activity, with applications in affective computing and neurofeedback. Approaches range from band-power and connectivity features to convolutional, recurrent, Transformer, and graph models, often using contrastive or masked pretraining, domain adaptation, and multimodal fusion with eye, face, or speech signals. Benchmarks such as SEED, DEAP, and FACED support tests within subjects and across subjects, sessions, and datasets, while some work predicts continuous emotional change or targets efficient deployment. A central challenge is transferring between people and settings while distinguishing useful neural signals from stimulus and context cues; reliable evaluation, interpretability, and real-world validation remain open concerns.