Single-Trial EEG-EMG Coherence Analysis Reveals Muscle Fatigue-Related Progressive Alterations in Corticomuscular Coupling

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 18; no. 2; pp. 97 - 106
• Main Authors: Yang, Qi, Siemionow, Vlodek, Yao, Wanxiang, Sahgal, Vinod, Yue, Guang H.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2010; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Cerebral Cortex - physiology; Coherence; Computer Simulation; Couplings; Data analysis; Data Interpretation, Statistical; electroencephalogram (EEG); Electroencephalography; Electroencephalography - statistics & numerical data; electromyogram (EMG); Electromyography; Electromyography - statistics & numerical data; Fatigue; Female; Fourier Analysis; Frequency; Hand Strength - physiology; Humans; Male; Muscle Fatigue - physiology; Muscle, Skeletal - physiology; Muscles; Muscular system; Wavelet transforms
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2010.2047173

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.