Functional Brain Variability Predicts Cognitive Performance Independent of Mean EEG Power

Background: Traditional electroencephalography (EEG) analysis focuses on mean spectral power, which may overlook the functional significance of neural signal variability. Emerging perspectives posit that moment-to-moment neural variability—captured through complexity metrics and temporal dynamics—is a key marker of adaptive brain function and cognitive efficiency. Objective: This study aimed to determine whether multiple indices of functional brain dynamics in the alpha band (8–13 Hz) predict cognitive performance consistency independently of mean alpha power in healthy medical students, using both traditional variability metrics and contemporary complexity measures. Methods: In a cross-sectional design, 52 participants (mean age 23.1 years; 50% female) underwent resting and task EEG recording. Alpha power and its trial-to-trial variability (coefficient of variation, CoV) were computed. Additionally, multiscale permutation entropy (MPE) and detrended fluctuation analysis (DFA) were applied to quantify signal complexity and long-range temporal correlations. Cognitive performance was assessed via a reaction time task, with intra-individual variability (RT SD) as the primary outcome. Stress was measured using a Visual Analogue Scale and physiological reactivity (heart rate change) during a Mental Arithmetic Test. Relationships were examined using correlation, hierarchical regression, and multi-feature prediction models incorporating quadratic (nonlinear) effects. Results: Alpha variability (CoV) was significantly correlated with RT SD (r = 0.42, p = 0.001) and mean RT (r = 0.27, p = 0.049). Multiscale entropy in the alpha band showed a significant inverted-U relationship with RT variability (R² = 0.21, p = 0.003 for quadratic term), indicating that moderate complexity was associated with greatest performance stability. DFA exponents correlated negatively with RT variability (r = -0.29, p = 0.038), suggesting that stronger long-range temporal correlations (closer to critical dynamics) relate to more consistent performance. Perceived stress and stress reactivity also correlated with RT SD (r = 0.29, p = 0.034 and r = 0.32, p = 0.022, respectively). Hierarchical regression confirmed alpha variability as a unique predictor of RT variability (β = 0.42, p = 0.001), accounting for 17% additional variance after controlling for mean alpha power, which was non-significant. A combined multi-feature model including CoV, quadratic MPE, and DFA explained 28% of variance in RT variability—substantially more than any single metric alone. Conclusion: Variability, complexity, and temporal correlations of alpha oscillations—not mean power—are significant neural correlates of performance stability. Multi-feature approaches incorporating dynamical metrics provide richer characterization of brain-behavior relationships, supporting the growing emphasis on neural dynamics in cognitive neuroscience. The inverted-U relationship between complexity and performance suggests an optimal range for cognitive stability, with deviations in either direction conferring risk for attentional inconsistency.