Latest AI and machine learning research in neurology for healthcare professionals.
Major depressive disorder (MDD), a debilitating mental illness, could cause functional disabilities and could become a social problem. An accurate and early diagnosis for depression could become challenging. This paper proposed a machine learning framework involving EEG-derived synchronization likelihood (SL) features as input data for automatic diagnosis of MDD. It was hypothesized that EEG-based...
BACKGROUND: There are limited data on the use of artificial intelligence methods for the diagnosis of dementia in epidemiological studies in low- and middle-income country (LMIC) settings. A culture and education fair battery of cognitive tests was developed and validated for population based studies in low- and middle-income countries including India by the 10/66 Dementia Research Group.
Identifying individuals destined to develop Alzheimer's dementia within time frames acceptable for clinical trials constitutes an important challenge ...
OBJECTIVE: In postanoxic coma, EEG patterns indicate the severity of encephalopathy and typically evolve in time. We aim to improve the understanding ...
On a voluntary basis, 10 adolescents with hemiparesis due to cerebral palsy and 11 neurologically healthy control subjects participated in this proof-...
This paper presents a novel application of an assistive robotic system with virtual assistance to enhance manual performance of individuals with cereb...
Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of...
BACKGROUND: Dementia is a complex disorder characterized by poor outcomes for the patients and high costs of care. After decades of research little is...
OBJECTIVES: To investigate the affective, social, behavioral, and physiological effects of the companion robot Paro for people with dementia in both a...
The purpose of this paper is to outline a novel method of using template based cross-correlation to identify and track uterine contractions during lab...
BACKGROUND: We examined the validity and reliability of a short robotic test of upper limb proprioception, the Arm Movement Detection (AMD) test, whic...
Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of f...
Mood disorders are a highly prevalent group of mental disorders causing substantial socioeconomic burden. There are various methodological approaches ...
Connexin43 mimetic peptide (Cx43MP) has been intensively investigated for its therapeutic effect in the management of inflammatory eye conditions, spi...
A new parametric approach is proposed for nonlinear and nonstationary system identification based on a time-varying nonlinear autoregressive with exog...
EEG is a standard non-invasive technique used in neural disease diagnostics and neurosciences. Frequency-tagging is an increasingly popular experiment...
. Error-free diagnosis of Alzheimer's disease (AD) from healthy control (HC) patients at an early stage of the disease is a major concern, because inf...
We introduce the feature-weighted receptive field (fwRF), an encoding model designed to balance expressiveness, interpretability and scalability. The ...
OBJECTIVE: To investigate the effects of robot-assisted training on the recovery of people with spinal cord injury (SCI).
BACKGROUND: Spasticity is a common sequela of stroke. Traditional assessment methods include relatively coarse scales that may not capture all charact...