Benchmarking Baseline Machine Learning Models for Multiclass EEG Emotion Classification on the FACED Dataset

Emotion recognition from electroencephalography (EEG) signals is a central problem in affective computing. This study benchmarks seven classical machine learning models—XGBoost, LightGBM, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, and Logistic Regression—for multiclass EEG emotion classification on the FACED dataset, which provides fine-grained labels across nine emotion categories (amusement, inspiration, joy, tenderness, anger, fear, disgust, sadness, and neutral) from 123 participants. We extract Differential Entropy (DE) features across five frequency bands and, using an identical feature-and-preprocessing pipeline, evaluate all models under three distinct protocols that differ only in how the data are partitioned: (i) an intra-subject protocol, in which a separate model is trained and tested within each participant and the per-participant scores are averaged; (ii) a pooled segment-level protocol, in which segments from all participants are mixed so that segments from the same participant may fall in both training and test sets; and (iii) a strict subject-independent protocol, in which all segments from any given participant are kept together on one side of the training and test split, so that test participants are never seen during training. Under the intra-subject protocol, the best model (LightGBM) reaches 85.4% accuracy; under the pooled segment-level protocol the best model reaches 56.4%; and under the subject-independent protocol the best model (SVM with an RBF kernel) achieves only 18.2% accuracy—above the 11.1% chance level but far below the other two settings. We provide reproducible baselines under all three protocols and argue that subject-independent evaluation is essential for meaningful benchmarking on FACED.