Key result
Integrated parallel gcForest-based EEG framework achieves ~95% peak accuracy for emotion recognition.
Why the study?
Deep-neural-network-based approaches for multi-channel EEG emotion recognition can involve substantial architectural and parameter-tuning complexity, while conventional cross-validation relies on pseudo-random fold construction.
The proposed parallel gcForest-based framework with BDSCV achieves high accuracy for EEG-based emotion recognition.
Answers are grounded in this paper and cite their sources.
Offers simpler EEG emotion classification than deep networks; leaves open external validation and clinical translation.
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.
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Lu et al. (2026) studied Emotion recognition. Parallel gcForest-based EEG emotion recognition framework with BDSCV was evaluated on Accuracy for arousal, valence, dominance, and liking. An integrated parallel gcForest-based EEG emotion recognition framework achieved accuracies of 93.65%, 94.78%, 93.23%, and 95.01% for arousal, valence, dominance, and liking on the DEAP dataset.
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