A novel method to generalize time-frequency coherence analysis between EEG or EMG signals during repetitive trials with high intra-subject variability in duration

Time-frequency coherence analysis between EEG and EMG signals represents a valuable tool to gain insight into neural mechanisms underlying motor control. However, for self-paced movements, the variability of inter-trial duration limits its proper use. To overcome this obstacle, we propose a time-normalizing approach and test it on both simulated and experimental data recorded during elbow extension movements performed by a post-stroke subject. Results show that the proposed time-normalization improves both the consistency and the accuracy of time-frequency coherence calculation, detection and quantification. The proposed time-normalization overcomes a major limitation to generalization of coherence analysis and can be suggested as an essential step to perform for coherence in presence of high intra-subject variability in duration.