Comparison of non-negative matrix factorization and convolution kernel compensation in surface electromyograms of forearm muscles

• Published in: 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP BMEI) pp. 1 - 5
• Main Authors: Savc, M., Glaser, V., Holobar, A., Cikajlo, I., Matjacic, Z.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2017
• Subjects: Electrodes; Electromyography; Low pass filters; Matrix decomposition; motor unit identification; Muscles; non-negative matrix factorization; surface electromyograms; Surface treatment; Wrist; wrist extension and flexion
• DOI: 10.1109/CISP-BMEI.2017.8302216

This contribution compares performances of nonnegative matrix factorization and high-density surface electromyogram (EMG) decomposition on EMG signals recoded from forearm muscles of young healthy subjects. During the EMG measurements, subjects performed dynamic wrist extensions and flexions and universal haptic device robot was used to oppose their movements and to measure wrist kinematics and excreted muscle forces. Recoded EMG signals were independently decomposed by Convolution Kernel Compensation technique and by alternating least squares non-negative matrix factorization. The identified motor unit discharge patterns were summed into cumulative spike trains and compared with non-negative components in each measurement. The results demonstrated good match (average correlation coefficient of 0.92 ± 0.06), but several discrepancies between the identified components have also been observed. In particular, when limiting the time support of identified components to active EMG signal portions only, the average correlation coefficient dropped to 0.72 ±0.20.