Corticomuscular Coherence Analysis on Hand Movement Distinction for Active Rehabilitation

Abstract

Active rehabilitation involves patient's voluntary thoughts as the control signals of restore device to assist stroke rehabilitation. Although restoration of hand opening stands importantly in patient's daily life, it is difficult to distinguish the voluntary finger extension from thumb adduction and finger flexion using stroke patients' electroencephalography (EMG) on single muscle activity. We propose to implement corticomuscular coherence analysis on electroencephalography (EEG) and EMG signals on Extensor Digitorum to extract their intention involved in hand opening. EEG and EMG signals of 8 subjects are simultaneously collected when executing 4 hand movement tasks (finger extension, thumb adduction, finger flexion, and rest). We explore the spatial and temporal distribution of the coherence and observe statistically significant corticomuscular coherence appearing at left motor cortical area and different patterns within beta frequency range for 4 movement tasks. Linear discriminate analysis is applied on the coherence pattern to distinguish finger extension from thumb adduction, finger flexion, and rest. The classification results are greater than those by EEG only. The results indicate the possibility to detect voluntary hand opening based on coherence analysis between single muscle EMG signal and single EEG channel located in motor cortical area, which potentially helps active hand rehabilitation for stroke patients.

Corticomuscular Coherence for Upper Arm Flexor and Extensor Muscles During Isometric Exercise and Cyclically Isokinetic Movement

“…It has also been indicated that when performed isometric exercise and dynamic concentric plantar flexion with sufficient ankle angle and forces, the discharging rate of the motor unit was significantly higher during the dynamic plantar flexion, which shown that the nature of the contraction affected the recruitment of the motor unit (Kallio et al, 2013). Previous studies focused on sustained muscle contractions or single contraction form (Divekar and John, 2013; Lou et al, 2013; Hu et al, 2018), which neglected the participation of sensorimotor cortex in relaxed or low-contraction nature muscles. In our study, we considered the whole contraction and relaxation stages of flexors and extensors acting as agonist and antagonist in continuous flexion-extension stage.…”

“…Most studies of upper arm focused on fingers, wrists, and elbows in isometric flexion or extension exercise. Lou et al (2013) observed significant corticomuscular coherence between the left motor cortical area of the EEG channels and extensor digitorum muscle during finger extension exercise, Divekar and John (2013) had established that wrist flexors revealed significantly lower peak beta-CMC levels compared with wrist extensors during high precision isometric wrist flexion and extension tasks. Bayram et al (2015) observed significant CMC for agonist and antagonist during sustained isometric elbow flexion, and agonistic muscles presented higher magnitude of CMC compared with antagonist muscles.…”

An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation

“…CMC is also found to be shifted from beta to gamma range during dynamic force output [33] and different task-specific modulations are found due to the demand of precision or muscle fatigue [34,35]. Different finger motion classification has also been done using CMC and researchers indicated its possible use for designing multimodal BCI system for the movement intention detection [36,37,38]. Although, CMC gives a good estimation of the functional interaction between the brain and muscle after averaging over…”

Detecting and classifying three different hand movement types through electroencephalography recordings for neurorehabilitation

“…Recently, it was demonstrated that MRCPs associated with movements performed with different levels of force and speed of the same body part (wrist and foot movements) could be decoded from the EEG using only information prior to the onset of the movement [13,14,21]. Also, different movement types have been classified such as hand grasping, opening and reaching [1,2,4], movement direction and kinematics (see [19] for a recent review), wrist movements [12,[40][41][42], shoulder and elbow movements [8,[47][48][49] and finger movements [26,27,44].…”