Advanced Signal Processing Techniques Applied to Cross-talk Reduction in Forearm S-emg

In spite of the great advances in the mechanical and electronic components of prosthetic hands, they still lack the high number of degrees of freedom present in the real human hand. That is due, not to technical deficiencies, but to the much reduced amount of independent control signals available when using surface electromyography (s-EMG) from the forearm stump or other artificial sensors. Cross-talk between adjacent muscles produces interferences that bury the s-EMG of the target muscle and reduce selectivity. In a single case study, surface-EMG signals from an able-bodied subject’s forearm were recorded with a surface, 5x13-electrode array while the subject performed eleven different isometric contractions. In order to reduce the cross-talk between s-EMG signals from different muscles, we applied a blind source separation (BSS) technique called JADE. Although the results are not fully conclusive, they indicate that BSS techniques could provide an important reduction in s-EMG cross-talk and hence BSS is able to increase the selectivity of recordings for myoelectric control.