A study on cortico-muscular coupling in finger motions for exoskeleton assisted neuro-rehabilitation

Abstract

In this paper our objective is to analyze the cortico-muscular coupling for hand finger motion and its possible use in the control of an exoskeleton based neurorehabilitation system for stroke sufferers. Cortical activity alone is often not sufficient to reliably control a device such as an exoskeleton and hence, our focus is to ascertain and analyze the connectivity between the motor cortex and forearm muscles, controlling the fingers, in terms of coherence between electroencephalogram (EEG) and electromyogram (EMG) signals. We have analyzed the signals separately for three different kinds of exercises consisting of passive motion of fingers using exoskeleton, active motion without any assistance, and motor imagery of the same movements. Four out of six healthy subjects who participated in the experiments have shown significant (p<;0.01) coherence for active finger motion which is well distinguished from the rest state. The EEG analysis resulted in average accuracy of 69.17% for passive finger motion with exoskeleton, 71.25% for active finger motion, and 67.92% for motor imagery, in detecting the volitional intention of the subjects to move their fingers. These results support that EEG-EMG coherence along with EEG analysis has the potential to make a more effective neurorehabilitation system for finger movement restoration of stroke sufferers.

Online Covariate Shift Detection-Based Adaptive Brain–Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation

“…µ= [8][9][10][11][12] Hz, β= [16][17][18][19][20][21][22][23][24] Hz). These two frequency ranges have been chosen empirically as they provide stable frequency response for Event-Related-Desynchronization or Event-Related-Synchronization (ERD/ERS) [43].…”

“…The strong correlation between EEG signals and mental tasks has led to many user centric applications such as virtual spellers for the communication [7], functional electrical stimulation (FES) based neuro-prosthesis for tetraplegics [4], hand exoskeleton control [8], [9], [10], [11] and telepresence for personal assistance [12]. In spite of the seemingly bright prospect of the BCI technology, there are some practical challenges regarding the robustness, accuracy, and information transfer rate (ITR) of such systems [13], [14], [15].…”

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

“…A c c e p t e d M a n u s c r i p t multiple trials, the single trial based estimation of CMC may not be sufficiently accurate to correctly trigger contingent neurofeedback. CMC needs longer epochs for having good frequency resolution, which is difficult to accommodate in a single-trial based neurofeedback generation as it would include a larger delay in the system [39,40,41]. This is primarily due to the inter-trial inconsistency of the CMC caused by the presence of non-stationary changes in the EEG data.…”

“…This is primarily due to the inter-trial inconsistency of the CMC caused by the presence of non-stationary changes in the EEG data. One of the major concerns is the reduction in maximum coherence value in case of stroke patients and its dynamic shift in frequency across different time segments [41,42]. Other pitfalls related to CMC includes lower information content, estimation vs. frequency resolution issues, and lower signal-to-noise ratio (SNR), especially for slow finger motions involving lower muscle mass.…”

Brain-Computer Interface Robotics for Hand Rehabilitation After Stroke: A Systematic Review

“…A final list with 30 studies was identified as eligible for qualitative review. Among the 30 studies, 11 [60][61][62][63][64][65][66][67][68][69][70] were involved in testing the BCI-hand robot system on chronic and subacute stroke patients ( [60,65] are RCTs) while the rest involved testing on healthy participants [71][72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89].…”