Latest AI and machine learning research in neurology for healthcare professionals.
OBJECTIVE: Electroencephalography (EEG) motor imagery classification has been widely used in healthcare applications such as mobile assistive robots and post-stroke rehabilitation. Recently, EEG motor imagery classification methods based on convolutional neural networks (CNNs) have been proposed and have achieved relatively high classification accuracy. However, these methods use single convolutio...
OBJECTIVE: Deep brain stimulation (DBS) of the internal segment of the globus pallidus (GPi) in patients with Parkinson's disease and dystonia improves motor symptoms and quality of life. Traditionally, pallidal borders have been demarcated by electrophysiological microelectrode recordings (MERs) during DBS surgery. However, detection of pallidal borders can be challenging due to the variability o...
OBJECTIVE: Electroencephalogram (EEG) signals are non-stationary. This could be due to internal fluctuation of brain states such as fatigue, frustrati...
Machine learning algorithms are becoming increasingly popular for decoding psychological constructs based on neural data. However, as a step towards b...
This paper dispenses an exhaustive review on deep learning techniques used in the prognosis of eight different neuropsychiatric and neurological disor...
BACKGROUND: The majority of stroke survivors experiences significant hand impairments, as weakness and spasticity, with a severe impact on the activit...
Adolescence is a time of extensive neural restructuring, leaving one susceptible to atypical development. Although neural maturation in humans can be ...
Neuromorphic computing based on spikes offers great potential in highly efficient computing paradigms. Recently, several hardware implementations of s...
OBJECTIVES: Compensations are commonly employed by patients with stroke during rehabilitation without therapist supervision, leading to suboptimal rec...
A subject of extensive research interest in the Brain Computer Interfaces (BCIs) niche is motor imagery (MI), where users imagine limb movements to co...
Background and Purpose- Selection of patients with acute ischemic stroke for endovascular treatment generally relies on dynamic susceptibility contras...
Background and Purpose- The aim of this study was to explore clinical and radiological prognostic factors for long-term swallowing recovery in patient...
Magnetic resonance (MR) imaging is a widely used imaging modality for detection of brain anatomical variations caused by brain diseases such as Alzhei...
Constrained chaos introduced into a three-module neural network having feedforward inter-module structure could have potential abilities to execute mu...
PURPOSE: Knowing the course of Alzheimer's disease is very important to prevent the deterioration of the disease, and accurate segmentation of sensiti...
An important barrier to commercialization of pattern recognition myoelectric control of prostheses is the lack of robustness to confounding factors su...
BACKGROUND: Motor impairment and loss of ambulatory function are major consequences of a spinal cord injury (SCI). Exoskeletons are robotic devices th...
BACKGROUND AND PURPOSE: Infarct volume is a valuable outcome measure in treatment trials of acute ischemic stroke and is strongly associated with func...
While prosthetic fitting after upper-limb loss allows for restoration of motor functions, it deprives the amputee of tactile sensations that are essen...
OBJECTIVE: To report the clinical and radiographic outcomes of single-stage posterior total en bloc spondylectomy (TES) of lumbar spinal metastases.