EMG collected during spinal cord transcutaneous stimulation

The aims of this study are to characterize the contamination of EMG signals by artifacts generated by the delivery of spinal cord transcutaneous stimulation (scTS) and to evaluate the performance of an Artifact Adaptive Ideal Filtering (AA-IF) technique to remove scTS artifacts from the EMG power spectrum. Methods: In five participants with spinal cord injury (SCI), scTS was delivered at different combinations of intensity (from 20 to 55mA) and frequencies (from 30 to 60 Hz) while Biceps Brachii (BB) and Triceps Brachii (TB) muscles were at rest or contracted. Using a Fast Fourier Transform, we characterized peak amplitude and frequency bands of scTS artifacts in the EMG signals recorded from BB and TB muscles. Then, we applied the AA-IF technique and the empirical mode decomposition Butterworth filtering method (EMD-BF) to remove the scTS artifacts from the EMG signals. Ultimately, we compared the content of the EMG power spectrum filtered out and the root mean square of the EMG signals (EMGrms) following application of the AA-IF and EMD-BF techniques. Results: An increase in the amplitude (p<0.001) and frequency width (p<0.001) of scTS artifacts was observed as current intensity delivered using scTS was increased and when the physiological content of the EMG signal was lower. Amplitude (p<0.01) and frequency width (p<0.001) of scTS artifacts were larger in the BB muscle compared to the TB muscle. Application of the AA-IF technique led to the removal of a lower amount of EMG power spectrum content compared to the EMD-BF technique (4±5% vs. 25±6%, p<0.001) and lower reduction in the amplitude of the EMGrms during both rest and contraction (p<0.001). Conclusion: The AA-IF technique allows a precise identification of the scTS artifacts from the FFT of the EMG signals and the implementation of ideal filters to ultimately preserve a larger amount of non-contaminated physiological content from the EMG signals.