Spatio-temporal analysis of surface electromyography signals by independent component and time-scale analysis

Recent work has demonstrated that the Independent Components (ICs) of simultaneously-recorded surface Electromyography (sEMG) recordings are more reliable in monitoring repetitive movements and better correspond with ongoing brain-wave activity than raw sEMG recordings. With this implementation, static spatial filters are used to create linear combinations of sEMGs that are maximally independent. We propose extending this formulation by applying time-scale analysis to the computed ICs to assess the critical assumption of stationarity of the spatial filters. We applied combined ICA/time-scale analyses to sEMGs recorded from the arm and shoulder from a single normal subject making repetitive pointing movements. Several of the ICs were clearly modulated in phase with the pointing movements, but still varied from trial-to-trial. Time-scale analysis allowed the extraction of that portion of each IC that was consistent across trials. Our results suggest that the ICs derived by static filters used in the ICA-only implementation may be affected by non-stationarities in the relation between sEMG signals, and that the combined ICA/wavelet approach is a practical means to extract highly reproducible features in sEMG recordings.