Brain–Machine Interface-Driven Post-Stroke Upper-Limb Functional Recovery Correlates With Beta-Band Mediated Cortical Networks

Abstract

Brain-machine interface (BMI) driven robot-assisted neurorehabilitation intervention has demonstrated improvement in upper-limb (UL) motor function, specifically, with post-stroke hemiparetic patients. However, neurophysiological patterns related to such interventions are not well understood. This study examined the longitudinal changes in band-limited resting-state (RS) functional connectivity (FC) networks in association with post-stroke UL functional recovery achieved by a multimodal intervention involving motor attempt (MA) based BMI and robotic hand-exoskeleton. Four adults were rehabilitated with the intervention for a period lasting upto 6 weeks. RS magnetoencephalography (MEG) signals, Action Research Arm Test (ARAT), and grip strength (GS) measures were recorded at five equispaced sessions over the intervention period. An average post-interventional increase of 100.0% (p = 0.00028) and 88.0% were attained for ARAT and GS, respectively. A cluster-based statistical test involving correlation estimates between beta-band (15−26 Hz) RS-MEG FCs and UL functional recovery provided positively correlated sub-networks in both contralesional and ipsilesional motor cortices. The fronto-parietal FC exhibited hemispheric lateralisation wherein majority of the positively and negatively correlated connections were found in contralesional and ipsilesional hemispheres, respectively. Our findings are consistent with the theory of bilateral motor cortical association with UL recovery and predict novel FC patterns that can be important for higher level cognitive functions.

Immediate and long-term effects of BCI-based rehabilitation of the upper extremity after stroke: a systematic review and meta-analysis

“…A total of 1015 records were screened, of which the full texts of 80 were assessed for eligibility. Finally, 33 studies were included in the current systematic review [6, 7, 15-17, 26, 35, 37, 46, 47, 49-71], of which 18 studies were of single-group design [26,37,[56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71], and 15 studies were of controlled-trial design [6, 7, 15-17, 35, 46, 47, 49-55]. In the current review, only studies with a controlled-trial design were included in our metaanalysis, and those with single-group designs were only included in our qualitative description.…”

Assessing impact of channel selection on decoding of motor and cognitive imagery from MEG data

“…These findings indicates that the alpha band may have more impact on classification performance in the context of motor imagery. However beta band is more related to the longitudinal changes in motor behaviour, as revealed by a study on post stroke rehabilitation [40]. CSP is a spatial filtering technique which needs larger number of channels to optimize the CSP projection matrix.…”

Deep Learning based Prediction of EEG Motor Imagery of Stroke Patients’ for Neuro-Rehabilitation Application

“…Thus, even if the performance of EEGNet is similar to FBCSP it is worth using if it could avoid the need for subject-specific tuning of the classifier and learn the features automatically from raw data. From our past experience of using BCI for neurorehabilitation [12], [32], we realised the need for calibrationfree BCI as the stroke patients are the most susceptible to get frustrated during repeated calibration. We have previously used covariate shift adaptation technique [11] to adapt the EEG classifier according to the shift in data distribution and tested its feasibility on stroke patients.…”

“…Comparison of the within subject average classification accuracy under different parameters: epochs = [100, 300, 500]; dropout = [025, 0.5], and kernel length =[32, 64, 128] …”