Quantum-Kernel Learning for Subject-Independent EEG Emotion Recognition with Spectral-Connectivity Features

Electroencephalography (EEG)-based emotion recognition remains challenging under subject-independent evaluation because of substantial inter-subject variability. This study investigates the feasibility of state-fidelity-based quantum-kernel learning for subject-independent valence and arousal recognition using differential entropy (DE) and phase-locking value (PLV) features. Using the DEAP dataset, DE and PLV features were extracted and evaluated individually and in combination, with subject-wise normalization and F-score feature selection applied within the evaluation framework. Six features were retained for valence and eight for arousal. The selected features were encoded using a Qiskit ZFeatureMap, and the fidelity-based quantum kernel was evaluated through statevector simulation on a classical computer. Quantum-kernel, Linear, and RBF SVMs were compared using nested participant-grouped cross-validation across DE, PLV, and DE–PLV feature representations. For valence, the quantum-kernel SVM using DE features achieved the highest mean balanced accuracy of 59.18%. For arousal, the Linear SVM using DE features achieved the highest mean balanced accuracy of 54.16%. Overall, the three classifiers showed comparable performance across the evaluated feature representations. These results support the feasibility of state-fidelity-based quantum similarity for subject-independent EEG emotion recognition.