Neurology

Sparse Bayesian Learning for Obtaining Sparsity of EEG Frequency Bands Based Feature Vectors in Motor Imagery Classification.

Effective common spatial pattern (CSP) feature extraction for motor imagery (MI) electroencephalogram (EEG) recordings usually depends on the filter band selection to a large extent. Subband optimization has been suggested to enhance classification accuracy of MI. Accordingly, this study introduces a new method that implements sparse Bayesian learning of frequency bands (named SBLFB) from EEG for ...

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks.

Feedforward deep neural networks (DNNs), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography...

Service innovation through social robot engagement to improve dementia care quality.

Assistive technologies, such as robots, have proven to be useful in a social context and to improve the quality of life for people with dementia (PwD)...

Multi-modal vertebrae recognition using Transformed Deep Convolution Network.

Automatic vertebra recognition, including the identification of vertebra locations and naming in multiple image modalities, are highly demanded in spi...

An EEG-Based Fuzzy Probability Model for Early Diagnosis of Alzheimer's Disease.

Alzheimer's disease is a degenerative brain disease that results in cardinal memory deterioration and significant cognitive impairments. The early tre...

RegenBase: a knowledge base of spinal cord injury biology for translational research.

Spinal cord injury (SCI) research is a data-rich field that aims to identify the biological mechanisms resulting in loss of function and mobility afte...

Robotic gait assistive technology as means to aggressive mobilization strategy in acute rehabilitation following severe diffuse axonal injury: a case study.

INTRODUCTION: Diffuse axonal injury is a prominent cause of disablement post-traumatic brain injury. Utilization of the rapid expansion of our current...

Pattern recognition for electroencephalographic signals based on continuous neural networks.

This study reports the design and implementation of a pattern recognition algorithm to classify electroencephalographic (EEG) signals based on artific...

Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer's Disease.

Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimer's Disease (AD), which, in turn, allows the applicati...

Using neuroimaging to help predict the onset of psychosis.

The aim of this review is to assess the potential for neuroimaging measures to facilitate prediction of the onset of psychosis. Research in this field...

Test and Validation of a Smart Exercise Bike for Motor Rehabilitation in Individuals With Parkinson's Disease.

To assess and validate the Smart Exercise Bike designed for Parkinson's Disease (PD) rehabilitation, 47 individuals with PD were randomly assigned to ...

Optimal training dataset composition for SVM-based, age-independent, automated epileptic seizure detection.

Automated seizure detection is a valuable asset to health professionals, which makes adequate treatment possible in order to minimize brain damage. Mo...

Classifying Response Correctness across Different Task Sets: A Machine Learning Approach.

Erroneous behavior usually elicits a distinct pattern in neural waveforms. In particular, inspection of the concurrent recorded electroencephalograms ...

tDCS and Robotics on Upper Limb Stroke Rehabilitation: Effect Modification by Stroke Duration and Type of Stroke.

Objective. The aim of this exploratory pilot study is to test the effects of bilateral tDCS combined with upper extremity robot-assisted therapy (RAT)...

Group activity with Paro in nursing homes: systematic investigation of behaviors in participants.

BACKGROUND: A variety of group activities is promoted for nursing home (NH) residents with dementia with the aim to reduce apathy and to increase enga...

Diabetic peripheral neuropathy class prediction by multicategory support vector machine model: a cross-sectional study.

OBJECTIVES: Diabetes is increasing in worldwide prevalence, toward epidemic levels. Diabetic neuropathy, one of the most common complications of diabe...

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.

Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modal...

A Novel Tool for Evaluation of Mild Traumatic Brain Injury Patients in the Emergency Department: Does Robotic Assessment of Neuromotor Performance Following Injury Predict the Presence of Postconcussion Symptoms at Follow-up?

OBJECTIVES: Postconcussion symptoms (PCS) are a common complication of mild traumatic brain injury (TBI). Currently, there is no validated clinically ...

Rehabilitative Soft Exoskeleton for Rodents.

Robotic exoskeletons provide programmable, consistent and controllable active therapeutic assistance to patients with neurological disorders. Here we ...

Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization.

Electroencephalography (EEG) headsets are the most commonly used sensing devices for brain-computer interface. In real-world applications, there are a...