Impact of Fatigue on Corticomuscular Coupling and EEG Microstates during Human-Robot Interaction and Physical Exercise

Human movements are controlled by the Central Nervous System. Motor dysfunction and sensory-motor deficits result in restricted use of the upper extremities in stroke patients, leading to difficulty in carrying out daily activities. Human-robot interaction (HRI) in rehabilitation can provide individualised, task oriented therapy to patients for fast recovery. The key goals of rehabilitation strategies are to enhance the functional ability and cognitive performance of stroke patients in an optimised way. Therapy can often benefit from assessing progress. Nine Hole Peg Test (NHPT) is one of the widely used tests for assessing upper extremity impairment whose only outcome measure is the time for completion of the task. Coordination between EEG and EMG signals plays a vital role in movement control. EEGEMG coherence is considered capable of measuring the control of spinal motor neurons by the cerebral cortex. It helps to understand how the brain controls muscle movement and also the effects of muscle movement on brain function hence can give more insight into fatigue. EEG microstates are recurrent scalp potential configurations that remain stable for a short period of time. The analysis of EEG microstates can help to identify the background neuronal activity at the millisecond level. Analysing EEG-EMG coherence and EEG microstates on a person performing NHPT under fatigue conditions will help us to have a better understanding of underlying neuronal activities. To explore these an experiment was conducted with 8 healthy participants while interacting with a robot-assisted NHPT. The experiment involved two trials of NHPT, then a fatiguing exercise which was then followed by two more trials of NHPT. EEG-EMG coherence was examined for pre fatigue and post fatigue trials of NHPT. EEG microstates analysis was conducted for resting state conditions, NHPT trial, and also during physical exercise. The analysis of EEG-EMG coherence showed an increase in corticomuscular coupling with fatigue. The increased EEG-EMG coherence suggests that the functional coupling between the brain and muscles becomes stronger with fatigue. Three distinct microstates were observed for the different physical states of participants. Changes were assessed by utilising microstate parameters such as occurrence, coverage, duration, and global explained variance. It was found that the coverage of some microstates is impacted by fatigue in all the experimental stages used for analysis. These results support the involvement of different neural assemblies but also highlight the potential that physical fatigue can be observed and identified by assessing changes in microstate parameters.