MELF: A multi-view ensemble learning framework for normative resting state EEG signal quality assessment

OBJECTIVE: Normative resting-state electroencephalography (rsEEG), defined as standardized recordings from healthy populations, provides essential baselines for studying brain mechanisms and psychiatric disorders. However, its reliability critically depends on rigorous quality assessment, as artifacts or over-processing can distort subsequent analyses. Current approaches often rely on single-feature perspectives, which fail to capture the multidimensional nature of signal quality. APPROACH: We proposes MELF, a quality assessment framework for normative rsEEG that systematically integrates three complementary feature domains: time domain (mobility, complexity), frequency domain (α-band relative power, parallel log spectra index), and spatial domain (α-band directed phase lag index, bad channel ratio, ICLabel-classified brain component proportion). A random forest-based ensemble classifier was constructed using dynamic weight allocation based on cross-validation accuracy. MAIN RESULTS: Experiments on four open rsEEG datasets (DSTR, TRCS, LEMON, HBN) and expert-annotated clinical data demonstrated that: (1) MELF achieved up to 10% accuracy improvement over single-view methods; (2) MELF outperformed traditional machine learning and deep learning baselines while reducing computational time, reaching 97.69% accuracy (AUC = 0.9957), and maintained strong performance on an independent test set (92% accuracy, AUC = 0.88); (3) the spectral view contributed the most, confirming the evaluative capability of the parallel log spectra index (PaLOSi). SIGNIFICANCE: MELF provides an interpretable, high-accuracy, and robust automated framework for normative rsEEG quality assessment, with broad applications in neuroscience research and clinical quality control.