Introduction: Application of biofeedback techniques in rehabilitation has turned into an exciting research area during the recent decade. Providing an appropriate visual or auditory biofeedback signal is the most critical requirement of a biofeedback technique. In this regard, changes in Surface Electromyography (SEMG) signals during wrist movement can be used to generate an indictable visual biofeedback signal for wrist movement rehabilitation via SEMG biofeedback. This paper proposes a novel methodology for selecting the most appropriate features out of wrist muscle SEMG signals. 
Methods: To this end, the surface EMG signals from flexor and extensor muscle groups during wrist joint movements were recorded and analyzed. Some linear and nonlinear features in frequency, time, and time-frequency domains were extracted from the recorded surface EMG signals of the flexor and extensor muscles. Experiments and analyses were performed on ten healthy subjects and four stroke patients with wrist muscle spasticity as the movement disorder subjects. Some heuristic feature selection measures were applied. The main motivation behind choosing applied heuristic feature selection measures was meeting. In the first step, the designed visual biofeedback signal should indicate a healthy wrist motion profile as its successful tracking by the patient guarantees rehabilitation. In addition, the visual biofeedback signal should be a smooth curve thus preventing the patient from discomfort while tracking it on a monitor during the biofeedback therapy. 
Results: In this pilot study, after using the introduced feature selection measures, quantitative and qualitative analyses of the extracted features indicated that Shannon entropy is the most appropriate feature for generating a visual biofeedback signal as a healthy wrist motion profile to improve the ability of stroke patients in controlling wrist joint motion. In addition, it was shown that when the wrist joint moves between a flexed and rest position, the flexor muscle EMG signal should be used for generating a visual biofeedback signal. However when the wrist joint moves between a rest position and an extended position, the extensor muscle EMG signal is appropriate for providing a visual biofeedback signal. It is worth noting that the achieved pilot study results should be confirmed by the future studies with larger samples.
Conclusion: According to the obtained results, it can be concluded that among the analyzed features, the Shannon entropy was the most appropriate feature. It can be employed for generating a visual biofeedback signal for reduction of spasticity in patients with stroke.