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 EMG signals. Methods : In five participants with spinal cord injury (SCI), scTS was delivered at different combinations of intensity (from 20 to 55 mA) and frequencies (from 30 to 60 Hz) while Biceps Brachii (BB) and Triceps Brachii (TB) muscles were at rest or voluntarily activated. Using a Fast Fourier Transform (FFT), we characterized peak amplitude of scTS artifacts and boundaries of contaminated frequency bands 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 identify and remove scTS artifacts. Finally, we compared the content of the FFT that was preserved and the root mean square of the EMG signals (EMGrms) following application of the AA-IF and EMD-BF techniques. Results : Frequency bands of ~2Hz width were contaminated by scTS artifact at frequencies nearby the main frequency set for the stimulator and its harmonics. The width of the frequency bands contaminated by scTS artifacts increased with current intensity delivered using scTS (p<0.001), was lower when EMG signals were recorded during voluntary contractions compared to rest (p<0.05), and was larger in BB muscle compared to TB muscle (p<0.001). A larger portion of the FFT was preserved using the AA-IF technique compared to the EmD-BF technique (96±5% vs. 75±6%, p<0.001). Conclusion : The AA-IF technique allows for a precise identification of the frequency bands contaminated by scTS artifacts and ultimately preserves a larger amount of uncontaminated content from the EMG signals.