Methodological approach for assessing motor cortical excitability changes with single-pulse transcranial magnetic stimulation.

Transcranial Magnetic Stimulation (TMS) serves as a crucial tool in evaluating motor cortex excitability by applying short magnetic pulses to the skull, inducing neuron depolarization in the cerebral cortex through electromagnetic induction. This technique leads to the activation of specific skeletal muscles recorded as Motor-Evoked Potentials (MEPs) through electromyography. Although various methodologies assess cortical excitability with TMS, measuring MEP amplitudes offers a straightforward approach, especially when comparing excitability states pre- and post-interventions designed to alter cortical excitability.

A comprehensive guide to BCI-based stroke neurorehabilitation interventions.

Brain-Computer Interfaces (BCIs) offer the potential to facilitate neurorehabilitation in stroke patients by decoding user intentions from the central nervous system, thereby enabling control over external devices. Despite their promise, the diverse range of intervention parameters and technical challenges in clinical settings have hindered the accumulation of substantial evidence supporting the efficacy and effectiveness of BCIs in stroke rehabilitation. This article introduces a practical guide designed to navigate through these challenges in conducting BCI interventions for stroke rehabilitation.

Continuous versus discrete robotic feedback for brain-computer interfaces aimed for neurorehabilitation.

Introduction: Brain-Computer Interfaces (BCI) can allow control of external devices using motor imagery (MI) decoded from electroencephalography (EEG). Although BCI have a wide range of applications including neurorehabilitation, the low spatial resolution of EEG, coupled to the variability of cortical activations during MI, make control of BCI based on EEG a challenging task.

Methods: An assessment of BCI control with different feedback timing strategies was performed.

A case report: Upper limb recovery from stroke related to SARS-CoV-2 infection during an intervention with a brain-computer interface.

COVID-19 may increase the risk of acute ischemic stroke that can cause a loss of upper limb function, even in patients with low risk factors. However, only individual cases have been reported assessing different degrees of hospitalization outcomes. Therefore, outpatient recovery profiles during rehabilitation interventions are needed to better understand neuroplasticity mechanisms required for upper limb motor recovery.

Brain-Computer Interface Coupled to a Robotic Hand Orthosis for Stroke Patients' Neurorehabilitation: A Crossover Feasibility Study.

Brain-Computer Interfaces (BCI) coupled to robotic assistive devices have shown promise for the rehabilitation of stroke patients. However, little has been reported that compares the clinical and physiological effects of a BCI intervention for upper limb stroke rehabilitation with those of conventional therapy. This study assesses the feasibility of an intervention with a BCI based on electroencephalography (EEG) coupled to a robotic hand orthosis for upper limb stroke rehabilitation and compares its outcomes to conventional therapy.

Prognosis of stroke upper limb recovery with physiological variables using regression tree ensembles.

This study assesses upper limb recovery prognosis after stroke with solely physiological information, which can provide an objective estimation of recovery.Clinical recovery was forecasted using EEG-derived Event-Related Desynchronization/Synchronization and coherence, in addition to Transcranial Magnetic Stimulation elicited motor-evoked potentials and upper limb grip and pinch strength. A Regression Tree Ensemble predicted clinical recovery of a stroke database (= 10) measured after a two-month intervention with the Fugl-Meyer Assessment for the Upper Extremity (FMA-UE) and the Action Research Arm Test (ARAT).

Automatic selection and feature extraction of motor-evoked potentials by transcranial magnetic stimulation in stroke patients.

Transcranial magnetic stimulation (TMS) allows the assessment of stroke patients' cortical excitability and corticospinal tract integrity, which provide information regarding motor function recovery. However, the extraction of features from motor-evoked potentials (MEP) elicited by TMS, such as amplitude and latency, is performed manually, increasing variability due to observer-dependent subjectivity. Therefore, an automatic methodology could improve MEP analysis, especially in stroke, which increases the difficulty of manual MEP measurements due to brain lesions.

Computer-aided diagnosis based on hand thermal, RGB images, and grip force using artificial intelligence as screening tool for rheumatoid arthritis in women.

Rheumatoid arthritis (RA) is an autoimmune disorder that typically affects people between 23 and 60 years old causing chronic synovial inflammation, symmetrical polyarthritis, destruction of large and small joints, and chronic disability. Clinical diagnosis of RA is stablished by current ACR-EULAR criteria, and it is crucial for starting conventional therapy in order to minimize damage progression. The 2010 ACR-EULAR criteria include the presence of swollen joints, elevated levels of rheumatoid factor or anti-citrullinated protein antibodies (ACPA), elevated acute phase reactant, and duration of symptoms.

Brain-computer interface performance analysis of monozygotic twins with discordant hand dominance: A case study.

Brain-computer interfaces (BCI) decode user's intentions to control external devices. However, performance variations across individuals have limited their use to laboratory environments. Handedness could contribute to these variations, especially when motor imagery (MI) tasks are used for BCI control.

Longitudinal Analysis of Stroke Patients' Brain Rhythms during an Intervention with a Brain-Computer Interface.

Stroke is a leading cause of motor disability worldwide. Upper limb rehabilitation is particularly challenging since approximately 35% of patients recover significant hand function after 6 months of the stroke's onset. Therefore, new therapies, especially those based on brain-computer interfaces (BCI) and robotic assistive devices, are currently under research.

Motor Imagery-Based Brain-Computer Interface Coupled to a Robotic Hand Orthosis Aimed for Neurorehabilitation of Stroke Patients.

Motor imagery-based brain-computer interfaces (BCI) have shown potential for the rehabilitation of stroke patients; however, low performance has restricted their application in clinical environments. Therefore, this work presents the implementation of a BCI system, coupled to a robotic hand orthosis and driven by hand motor imagery of healthy subjects and the paralysed hand of stroke patients. A novel processing stage was designed using a bank of temporal filters, the common spatial pattern algorithm for feature extraction and particle swarm optimisation for feature selection.