An adaptive filter for surface EMG measurement during FES

Biopotential signals have been used as command and feedback signals in systems using functional electrical stimulation (FES) of motor nerves to restore the lost function. To use the voluntary electromyography (EMG) as a control signal for the FES of the same muscle, it is necessary to suppress the M-wave evoked by the electrical stimulation. The methods used in previous studies are prone to excessive elimination of signals or residual artifacts. When the experimental environment changes, changes in many parameters will lead to inconvenience in application. In view of the shortcomings of the existing technology, this paper proposes an adaptive filter for surface EMG measurement during electrical stimulation. The filter filtering out electrical stimulation artifacts from surface EMG signals using templates that can adapt to different experimental individuals and changes in electrode positions. There are no special requirements for the degree of aliasing of muscle electrical signals. It enables online filtering synchronized with electrical stimulation and can be used in real-time systems. The experimental results on 7 healthy subjects suggested that the filter can effectively filter out electrical stimulation artifacts without deteriorating EMG.