The Neural Correlates and Behavioral Impact of Peripheral Noise Electrical Stimulation on Motor Learning

Somatosensory input plays a critical role in motor learning. Noise reduces the neural activation threshold and enhances the sensitivity of sensory neurons. While research has demonstrated that peripheral electrical stimulation with noise waveform improves motor performance and functions, the effects of noise electrical stimulation on motor learning remain unknown. This study aimed to investigate the immediate effects of peripheral noise electrical stimulation on motor learning and corresponding neural activities in the motor cortex. Eighteen healthy adults participated in 2 experimental sessions (i.e., noise and sham electrical stimulation conditions) on 2 separate days. Participants performed a grip force tracking task to follow a 0.5 Hz continuous sine wave with amplitudes of 10, 20, and 30% maximal voluntary isometric contraction while the electroencephalogram (EEG) of the sensorimotor cortex and the electromyography (EMG) of the right finger flexors were recorded. The differences (force error) between the actual and the targeted force were calculated, and motor learning was achieved by reducing the force error to a plateau. The efficiency of motor learning was defined as how fast the force error reached a plateau. Two-way (conditions [noise vs sham stimulation] by time [during vs post]) analysis of variance with repeated measures was used to compare the differences in force error, EEG power spectrum density (PSD), and EEG-EMG (corticomuscular) coherence (CMC). The significance level was set at 0.05. Noise electrical stimulation significantly reduced the force error both during and post motor learning (p <0.05)>30 Hz) CMC. We also observed that motor learning resulted in a decrease in EEG PSD beta band and gamma CMC. This study demonstrated that noise electrical stimulation during motor learning significantly reduced the time required to learn a motor task. We also identified neurophysiological signatures that associate with motor learning, including desynchronization of EEG beta power and reduced functional connectivity between the brain and muscles. These findings could potentially help develop novel motor training strategies and precision interventions.