Systematic Literature Review of Machine Learning Methods for Emotion Recognition Using EEG and Physiological Signals in Healthcare

Emotion recognition (ER) from physiological signals, particularly electroencephalography (EEG), is increasingly studied for healthcare uses such as mental health monitoring, stress assessment, and patient-adaptive support. Compared with behavioral observation alone, biosignals including EEG, electrocardiography (ECG), electrodermal activity (EDA), photoplethysmography (PPG; reported as blood volume pulse, BVP), and respiration provide objective correlates of affect when overt cues are limited or unreliable. This systematic review synthesizes 70 peer-reviewed studies published from 2014 to 2024. We summarize 1) physiological modalities and multimodal fusion strategies, 2) machine learning and deep learning methods most frequently used, 3) datasets, evaluation protocols, and metrics with explicit separation of subject-dependent (SD) and subject-independent (SI) or leave-one-subject-out (LOSO) settings, and 4) explainable AI (XAI) practices that aim to improve interpretability and clinical trust. Across the literature, SD results are consistently higher than SI/LOSO results; domain adaptation and multimodal fusion partially close this gap. Paired within-study contrasts show small but reliable gains on DEAP for fusion over EEG alone by about 3 to 5 percentage points, and larger gains of about 25 to 35 percentage points on SEED-IV or SEED-V under participant-generic or LOSO protocols; WESAD also shows meaningful improvements. However, common barriers to clinical translation include artifacts and noise, inter-subject variability, limited cohort diversity and external validation, privacy and governance constraints, and nonstandardized interpretability. We outline directions for real-world adoption, including protocol-aware benchmarking, longitudinal and cross-site validation, streamlined sensor configurations, personalization through brief calibration or domain adaptation, and clinically useful XAI that supports auditability and aligns with healthcare regulatory expectations.