Emotion Recognition from Multi-Channel EEG via the Best-Discrepancy Systematic Cross-Validation and Parallel gcForest

Abstract

Multi-channel electroencephalogram (EEG) emotion recognition in intelligent human–computer interaction has gained substantial attention. However, deep-neural-network-based approaches can involve substantial architectural and parameter-tuning complexity. Conventional cross-validation uses pseudo-random fold construction, whereas best-discrepancy systematic cross-validation (BDSCV) is used here as a deterministic and systematic data-partitioning strategy for fold construction. We present an integrated parallel gcForest-based EEG emotion recognition framework that processes complementary 1D and 2D EEG representations in parallel and fuses their class-vector outputs. On the public Database for Emotion Analysis using Physiological Signals (DEAP) dataset, the framework reported accuracies of 93.65%, 94.78%, 93.23%, and 95.01% for arousal, valence, dominance, and liking, respectively. The reported accuracy and F1-score results indicate competitive recognition performance under the adopted subject-dependent segment-level evaluation setting.