Neurology

The Role of Audio-Visual Feedback in a Thought-Based Control of a Humanoid Robot: A BCI Study in Healthy and Spinal Cord Injured People.

The efficient control of our body and successful interaction with the environment are possible through the integration of multisensory information. Brain-computer interface (BCI) may allow people with sensorimotor disorders to actively interact in the world. In this study, visual information was paired with auditory feedback to improve the BCI control of a humanoid surrogate. Healthy and spinal co...

Neural correlates of motor recovery after robot-assisted stroke rehabilitation: a case series study.

Robot-assisted bilateral arm therapy (RBAT) has shown promising results in stroke rehabilitation; however, connectivity mapping of the sensorimotor networks after RBAT remains unclear. We used fMRI before and after RBAT and a dose-matched control intervention (DMCI) to explore the connectivity changes in 6 subacute stroke patients. Sensorimotor functions improved in the RBAT and DMCI groups after ...

Decoding post-stroke motor function from structural brain imaging.

Clinical research based on neuroimaging data has benefited from machine learning methods, which have the ability to provide individualized predictions...

Revealing disease-associated pathways by network integration of untargeted metabolomics.

Uncovering the molecular context of dysregulated metabolites is crucial to understand pathogenic pathways. However, their system-level analysis has be...

A Robotic Exoskeleton for Treatment of Crouch Gait in Children With Cerebral Palsy: Design and Initial Application.

Crouch gait, a pathological pattern of walking characterized by excessive knee flexion, is one of the most common gait disorders observed in children ...

Making use of longitudinal information in pattern recognition.

Longitudinal designs are widely used in medical studies as a means of observing within-subject changes over time in groups of subjects, thereby aiming...

Independent Component Analysis-Support Vector Machine-Based Computer-Aided Diagnosis System for Alzheimer's with Visual Support.

Computer-aided diagnosis (CAD) systems constitute a powerful tool for early diagnosis of Alzheimer's disease (AD), but limitations on interpretability...

Detecting epileptic seizures with electroencephalogram via a context-learning model.

BACKGROUND: Epileptic seizure is a serious health problem in the world and there is a huge population suffering from it every year. If an algorithm co...

Functional Relevance of Different Basal Ganglia Pathways Investigated in a Spiking Model with Reward Dependent Plasticity.

The brain enables animals to behaviorally adapt in order to survive in a complex and dynamic environment, but how reward-oriented behaviors are achiev...

Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features.

Recently, an increasing number of researchers have endeavored to develop practical tools for diagnosing patients with schizophrenia using machine lear...

Longitudinal clinical score prediction in Alzheimer's disease with soft-split sparse regression based random forest.

Alzheimer's disease (AD) is an irreversible neurodegenerative disease and affects a large population in the world. Cognitive scores at multiple time p...

Artificial neural networks to predict 3D spinal posture in reaching and lifting activities; Applications in biomechanical models.

Spinal posture is a crucial input in biomechanical models and an essential factor in ergonomics investigations to evaluate risk of low back injury. In...

A single robotic session that guides or increases movement error in survivors post-chronic stroke: which intervention is best to boost the learning of a timing task?

PURPOSE: Timing deficits can have a negative impact on the lives of survivors post-chronic stroke. Studies evaluating ways to improve timing post stro...

Efficacy of robot-assisted rehabilitation for the functional recovery of the upper limb in post-stroke patients: a randomized controlled study.

BACKGROUND: A prompt and effective physical and rehabilitation medicine approach is essential to obtain recovery of an impaired limb to prevent tendon...

Classification Preictal and Interictal Stages via Integrating Interchannel and Time-Domain Analysis of EEG Features.

The life quality of patients with refractory epilepsy is extremely affected by abrupt and unpredictable seizures. A reliable method for predicting sei...

The potential power of robotics for upper extremity stroke rehabilitation.

Two decades of research on robots and upper extremity rehabilitation has resulted in recommendations from systematic reviews and guidelines on their u...

Basal Ganglia dysfunctions in movement disorders: What can be learned from computational simulations.

The basal ganglia are a complex neuronal system that is impaired in several movement disorders, including Parkinson's disease, Huntington's disease, a...

NeuroRDF: semantic integration of highly curated data to prioritize biomarker candidates in Alzheimer's disease.

BACKGROUND: Neurodegenerative diseases are incurable and debilitating indications with huge social and economic impact, where much is still to be lear...

Motor Imagery Classification Based on Bilinear Sub-Manifold Learning of Symmetric Positive-Definite Matrices.

In motor imagery brain-computer interfaces (BCIs), the symmetric positive-definite (SPD) covariance matrices of electroencephalogram (EEG) signals car...

The adaptive drop foot stimulator - Multivariable learning control of foot pitch and roll motion in paretic gait.

Many stroke patients suffer from the drop foot syndrome, which is characterized by a limited ability to lift (the lateral and/or medial edge of) the f...