Introduction: Modern methods of analyzing surface electromyography (sEMG) from a matrix of electrodes allow for detailed muscle activation maps and interaction detection. While these activity maps show muscle response, they do not reveal muscle coordination during different gestures. Creating muscle component activity maps can aid in the development of rehabilitation programs and the creation of bionic prostheses by identifying signal localization in specific muscles. Methods: This study was conducted with healthy subjects (N=5). Trigno avanti sEMG sensors (n=8) (Delsys Inc, USA) were used, arranged in a circle on the superficial muscles of the forearm. The gestures chosen for visualization were fist clenching, finger extension, and thumb elevation. Results: When analyzing the data associated with the muscle component activation maps for the five subjects, it is possible to observe differences in the pattern of muscle co-activation depending on the gesture. During fist clenching, the largest contraction amplitude is observed in m. extensor carpi ulnaris and m. palmaris longus. For finger extension, the greatest activity was observed in m. extensor carpi ulnaris and m. extensor digitorum. Finally, for thumb elevation, m. extensor carpi radialis longus and m. extensor carpi ulnaris were the most involved, and m. palmaris longus was also consistently active. The other selected muscles were not activated, indicating precise muscle coordination to perform the different gestures. Conclusions: Forearm muscles are differentially involved in muscle signal formation. The segmentation of muscle signals allows for specific signal acquisition for further use in prosthetics and rehabilitation.