title: Single-Trial EEG-EMG coherence analysis reveals muscle fatigue-related progressive alterations in corticomuscular coupling.
abstract: Voluntary muscle fatigue is a progressive process. A recent study demonstrated muscle fatigue-induced weakening of functional corticomuscular coupling measured by coherence between the brain [electroencephalogram (EEG)] and muscle [electromyogram (EMG)] signals after a relatively long-duration muscle contraction. Comparing the EEG-EMG coherence before versus after fatigue or between data of two long-duration time blocks is not adequate to reveal the dynamic nature of the fatigue process. The purpose of this study was to address this issue by quantifying single-trial EEG-EMG coherence and EEG, EMG power based on wavelet transform. Eight healthy subjects performed 200 maximal intermittent handgrip contractions in a single session with handgrip force, EEG and EMG signals acquired simultaneously. The EEG and EMG data during each 2-s handgrip was subjected to single trial EEG-EMG wavelet energy spectrum and coherence computation. The EEG-EMG coherence and energy spectrum at beta (15 ~ 35 Hz) and gamma (35-50 Hz) frequency bands were statistically analyzed in 2-block (75 trials per block), 5-block (30 trials/block), and 10-block (15 trials/block) data settings. The energy of both the EEG and EMG signals decreased significantly with muscle fatigue. The EEG-EMG coherence had a significant reduction for the 2-block comparison. More detailed dynamical changing and inter-subject variation of the EEG-EMG coherence and energy were revealed by 5- and 10-block comparisons. These results show feasibility of wavelet transform-based measurement of the EEG-EMG coherence and corresponding energy based on single-trial data, which provides extra information to demonstrate a time course of dynamic adaptations of the functional corticomuscular coupling, as well as brain and muscle signals during muscle fatigue.
authors.affiliation: Cleveland Clinic, Cleveland, OH 44195, USA.
authors.forename: Vlodek
authors.initials: V
authors.lastname: Siemionow
authors.forename: Vinod
authors.initials: V
authors.lastname: Sahgal
authors.forename: Guang H
authors.initials: GH
authors.lastname: Yue
mesh_terms: D000328:Adult; D000465:Algorithms; D002540:Cerebral Cortex / Q000502:physiology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000706:statistics & numerical data*; D004576:Electromyography / Q000706:statistics & numerical data*; D005260:Female; D005583:Fourier Analysis; D018737:Hand Strength / Q000502:physiology; D006801:Humans; D008297:Male; D018763:Muscle Fatigue / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*