Key result
Physical load during arm lifts alters corticomuscular coupling across all measured features.
Why the study?
Traditional coherence-based methods capture linear synchronisation and often overlook nonlinear and scale-dependent dynamics in neuromuscular control, prompting a framework using nonlinear and information-theoretic descriptors to quantify CMC across physical loads.
p-value: p=<0.01
Nonlinear and information-theoretic descriptors of EEG-EMG signals are sensitive to load-dependent neuromuscular changes during weightlifting.
Nonlinear CMC framework may refine neuromuscular coupling assessment; leaves open clinical validation and standardization.
Background and Objective Cortico-muscular coherence (CMC) reflects the functional coupling between cortical neural oscillations and muscular activity, typically assessed using simultaneously recorded Electroencephalography (EEG) and Electromyography (EMG). Traditional coherence-based methods primarily capture linear, frequency-specific synchronisation, often overlooking nonlinear and scale-dependent dynamics inherent in neuromuscular control. This study proposes a framework to quantify CMC using nonlinear and information-theoretic descriptors, and investigates how cortico-muscular interaction varies with physical load during visually cued flexion-extension tasks. Methods EEG and EMG signals were recorded simultaneously from five healthy right-handed male participants (mean age 26 ± 3 years) performing arm lifts under nine load conditions (no load to 5 kg). EEG was acquired from three cortical channels (C3, C4, Cz) at 256 Hz; EMG was recorded from two right-arm muscle groups — Biceps Brachii (M1) and Flexor Carpi Radialis (M2) — and downsampled to 256 Hz to match the EEG sampling rate. Correlations were computed across all six EEG–EMG channel pairs using three complementary descriptors — Multifractal Detrended Cross-Correlation Analysis (MF-DXA), Mutual Information (MI), and Joint Entropy (JEn) — yielding 18 features. Nine 60-second segments per subject, corresponding to the nine loading conditions, were extracted using visual-cue onset timestamps. Statistical significance was assessed using the Friedman test and Conover-Iman post hoc analysis (p ≤ 0.01). Results All 18 features showed statistically significant differences across loading conditions (p < 0.01) for all five participants, demonstrating that MF-DXA, MI, and JEn are sensitive to load-dependent changes in corticomuscular coupling. This is a preliminary study conducted on a small cohort; the limited sample size (five participants, nine loading conditions) precludes machine-learning-based generalisation, and any classification-style analysis is reported strictly as a feasibility observation rather than a validated result. Conclusion This study introduces a preliminary, cost-effective, and non-invasive methodology for quantifying cortico-muscular connectivity using nonlinear and information-theoretic descriptors. The proposed framework shows statistically significant sensitivity to load-dependent neuromuscular changes; clinical validation with a larger cohort is required before diagnostic claims can be made.
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Parbat et al. (2026) studied Healthy (n=5). Physical load during arm lifts vs. No load was evaluated on Differences in corticomuscular coupling features (MF-DXA, MI, JEn) across loading conditions (p=<0.01). Physical load during arm lifts significantly altered corticomuscular coupling across all 18 nonlinear and information-theoretic features (p < 0.01).
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