Detection of Muscle Specific EMG Signals in Post Stroke Patients

Electromyography (EMG) is an extremely useful tool in extracting control signals from the human body. Needle electromyography is the current standard for obtaining superior quality muscle signals and obtaining signals corresponding to individual muscles. However, needle EMG faces many problems when converting from the laboratory to marketable devices, specifically in home devices. Many patients have issues with needles and the extra care required of needle EMG is prohibitive. Therefore, a surface EMG device that can obtain clear signals from individual muscles would be valuable to many markets in the development of next generation in home devices. Here, signals from surface EMG were analyzed using a low noise EMG evaluation system (RHD 2000; Intan Technologies). The signal to noise ratio (SNR) was calculated using MatLab. The average SNR is 4.447 for the Extensor Carpi Ulnaris, and 7.369 for the Extensor Digitorum Communis. Spectral analysis was performed using the Welch approach in MatLab. The power spectrum indicated that low frequency signals dominate the EMG of small hand muscles. Also, harmonic bands of 60Hz noise were present as part of the signal which should be accounted for with filters in future iterations of the testing method. Provided is evidence that strong, independent signals were acquired and could be used in further application of surface EMG corresponding to lifting of the fingers.