Effects of Computerized Biofeedback-based Balance Intervention on the Muscle Coactivation Patterns during Dynamic Postural Control in Traumatic Brain Injury.

Balance Dysfunction (BDF) is a severe conse-quence of Traumatic Brain Injury (TBI) that significantly increases the falls risk. However, the neuromuscular mecha-nisms of the BDF are not adequately researched. Therefore, in this study, our objective was to investigate the effects of a Computerized Biofeedback-based Balance Intervention (CBBI) on the muscle coactivation patterns in a group of TBI participants. This study presents the findings from 13 TBI individuals randomized into the Intervention group (TBI - INT, N=6) and Control group (TBI-CTL, N=7). Using a computerized posturography platform (Neurocom Balance Master) during baseline and follow-up assessment visits, the participant's pos-tural response to anterior-posterior balance perturbations were recorded in a multimodal setup including electroencephalogra-phy (EEG), electromyography (EMG), and the platform sway in terms of center of pressure (COP). The muscle responses were recorded from lower-limb muscles, including tibialis an-terior (TA) and gastrocnemius (GAST), whose coactivation was computed using a metric called Co-Contraction Index (CCI). Clinical outcome measures such as Berg Balance Scale (BBS), 10 Meter Walk Test (10MWT), and Timed Up-and-Go (TUG) tests were used to evaluate functional balance and mobility. The comparison of CCI values across time points (baseline and follow-up) revealed a significant decrease (p<0.01) in the TBI-INT group but not TBI-CTL. The intervention-related changes in CCI correlated with the changes in BBS score (from baseline to follow-up). These preliminary findings demonstrate that the CBBI training may help postural stability by facilitating the coactivation between muscles involved in postural control. Clinical relevance- The current knowledge of changes in the neuromuscular response to balance perturbation in TBI is limited. Our study opens the possibility of using the muscle CCI metric to evaluate the muscle response in individuals with impaired balance.

An Automated Workflow for the Electric Field Modeling of High-definition Transcranial Direct Current Stimulation (HD-tDCS) in Chronic Stroke with Lesions.

Transcranial Direct Current Stimulation is a popular noninvasive brain stimulation (NIBS) technique that modulates brain excitability by means of low-amplitude electrical current (usually <4mA) delivered to the electrodes on the scalp. The NIBS research has gained significant momentum in the past decade, prompting tDCS as an adjunctive therapeutic tool for neuromuscular disorders like stroke. However, due to stroke lesions and the differences in individual neuroanatomy, the targeted brain region may not show the same response upon NIBS across stroke patients. To this end, we conducted a study to test the feasibility of targeted NIBS. The hand motor hotspot (HMH) for each chronic stroke participant was identified using Neuronavigated Transcranial Magnetic Stimulation (TMS). After identifying the HMH as the neural target site, we applied High-definition TDCS with the current delivered at 2mA for 20 minutes. To simulate the effects of HD-tDCS in the brain, especially with stroke lesions, we used the computational modeling tool (ROAST). The lesion mask was identified using an automated tool (LINDA). This paper demonstrates that the stroke lesions can be incorporated in the computational modeling of electric field distribution upon HD-tDCS without manual intervention.

Altered Modulation of the Movement-Related Beta Desynchronization with Force in Stroke - a Pilot Study.

Conventional therapy improves motor recovery after stroke. However, 50% of stroke survivors still suffer from a significant level of long-term upper extremity impairment. Identifying a specific biomarker whose magnitude scales with the level of force could help in the development of more effective, novel, highly targeted rehabilitation therapies such as brain stimulation or neurofeedback. Four chronic stroke participants were enrolled in this pilot study to find such a neural marker using an Independent Component Analysis (ICA)-based source analysis approach, and investigate how it has been affected by the injury. Beta band desynchronization in the ipsilesional primary motor cortex was found to be most robustly scaling with force. This activity modulation with force was found to be significantly reduced, and to plateau at higher force than that of the contralesional (unaffected) side. A rehabilitation therapy that would target such a neuromarker could have the potential to strengthen the brain-to-muscle drive and improve motor learning and recovery.Clinical Relevance- This study identifies a neural marker that scales with motor output and shows how this modulation has been affected by stroke.

Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular Disorders.

Goal: This article presents the design and validation of an accurate automatic diagnostic system to classify intramuscular EMG (iEMG) signals into healthy, myopathy, or neuropathy categories to aid the diagnosis of neuromuscular diseases. Methods: First, an iEMG signal is decimated to produce a set of "disjoint" downsampled signals, which are decomposed by the lifting wavelet transform (LWT). The Higuchi's fractal dimensions (FDs) of LWT coefficients in the subbands are computed. The FDs of LWT subband coefficients are fused with one-dimensional local binary pattern derived from each downsampled signal. Next, a multilayer perceptron neural network (MLPNN) determines the class labels of downsampled signals. Finally, the sequence of class labels is fed to the Boyer-Moore majority vote (BMMV) algorithm, which assigns a class to every iEMG signal. Results: The MLPNN-BMMV classifier was experimented with 250 iEMG signals belonging to three categories. The performance of the classifier was validated in comparison with state-of-the-art approaches. The MLPNN-BMMV has resulted in impressive performance measures (%) using a 10-fold cross-validation-accuracy = [Formula: see text], sensitivity (normal) = [Formula: see text], sensitivity (myopathy) = [Formula: see text], sensitivity (neuropathy) = [Formula: see text], specificity (normal) = [Formula: see text], specificity (myopathy) = [Formula: see text], and specificity (neuropathy) = [Formula: see text]-surpassing the existing approaches. Conclusions: A future research direction is to validate the classifier performance with diverse iEMG datasets, which would lead to the design of an affordable real-time expert system for neuromuscular disorder diagnosis.

Altered Cortical and Postural Response to Balance Perturbation in Traumatic Brain Injury - An EEG Pilot Study.

30-60% of traumatic brain injury (TBI) patients suffer from long-term balance deficit. Even though motor preparation and execution are altered and slowed in TBI, their relative contribution and importance to posture instability remain poorly understood. This study investigates the impaired cortical dynamics and neuromuscular response in TBI in response to balance perturbation and its relation to balance deficit. 12 TBI and 6 healthy control (HC) participants took the Berg Balance Scale (BBS) test and participated in a balance perturbation task where they were subjected to random anterior/posterior translation, while brain (EEG), muscle (EMG) activities, and center of pressure (COP) were continuously recorded. Using independent component analysis (ICA), the component most responsible for the N1 component of the perturbation evoked potential (PEP) was selected and its amplitude and latency were extracted. Balance task performance was measured by computing the COP displacement during the task. TBI had a significantly lower BBS, larger COP displacement and lower N1 amplitude compared to the HC group. No group differences was found for N1 latency and muscle activity onset delay to the perturbation. BBS was correlated with the COP displacement and N1 amplitude, and COP displacement was correlated with N1 latency. TBI balance deficit may be associated with more impaired than delayed cortical response to balance perturbation.

Multi-direction hand movement classification using EEG-based source space analysis.

Recent advances in the brain-computer interfaces (BCIs) have demonstrated the inference of movement related activity using non-invasive EEG. However, most of the sensorspace approaches that study sensorimotor rhythms using EEG do not reveal the underlying neurophysiological phenomenon while executing or imagining the movement with finer control. Therefore, there is a need to examine feature extraction techniques in the cortical source space which can provide more information about the task compared to sensor-space. In this study, we extend the traditional sensor-space feature extraction method, Common Spatial Pattern (CSP), to the source space, using various regularization approaches. We use Weighted Minimum Norm Estimate (wMNE) as a source localization technique. We show that for a multi-direction hand movement classification problem, the source space features can result in an increase of over 10% accuracy compared to sensor space features. Fisher's Linear Discriminant (FLD) classifier with the One-versus-rest approach is used for the classification.