Anatomy

Neurophysiology

Pattern recognition (psychology)

Neuroscience

Thumb

Biology

Dorsum

Wearable computer

Embedded system

Artificial intelligence

Psychology

Principal component analysis

Electromyography

Forearm

Motor unit

Computer science

Medicine

Physical medicine and rehabilitation

We propose a framework based on high-density surface electromyography (HD-sEMG) to identify the neural drive to muscles controlling the human hand. High-density (320 channels) sEMG signals were recorded concurrently from intrinsic (the four dorsal interossei and thenar) and extrinsic (forearm) hand muscles and then decomposed into the constituent trains of motor unit (MU) action potentials. The participants performed pinch tasks with simultaneous activation of the thumb and one of the other fingers with sinusoidal force variations. The common drive among MUs across different muscles was extracted via principal component analysis (PCA) of the smoothed MU discharge rates. The first principal component of the smoothed discharge rates of all identified motor neurons explained 48.7 ± 15.4% of the total variance across all pinching tasks, indicating a common neural input shared by different muscles of the forearm and the hand.. When considering only the MUs extracted from extrinsic and intrinsic muscles, the percent of variance explained was 48.3 ± 15.3% and 57.1 ± 15.5%, respectively. This framework is conceived to use motor neuron activity for a proportional myoelectric control and rehabilitation technologies. A wearable adaptation of the framework is proposed for future perspectives.

A high-density surface EMG framework for the study of motor neurons controlling the intrinsic and extrinsic muscles of the hand