Latest AI and machine learning research in neurology for healthcare professionals.
In the last decade robotic devices have been applied in rehabilitation to overcome walking disability in neurologic diseases with promising results. Robot assisted gait training (RAGT) using the Lokomat seems not only to improve gait parameters but also the perception of well-being. Data on the psychosocial patient-robot impact are limited, in particular in the real-world of RAGT, in the rehabilit...
Automatic seizure detection is extremely important in the monitoring and diagnosis of epilepsy. The paper presents a novel method based on dictionary pair learning (DPL) for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. First, for the EEG data, wavelet filtering and differential filtering are applied, and the kernel function is performed to make the signal ...
In this paper, we present a multimodal emotion recognition framework called EmotionMeter that combines brain waves and eye movements. To increase the ...
Wavelet Neural Networks are a combination of neural networks and wavelets and have been mostly used in the area of time-series prediction and control....
The purpose of this study was to critically evaluate the effects of robot-assisted gait training (RAGT) on gait-related function in patients with acut...
CONTEXT: Robotic assisted gait training (RAGT) technology can be used as a rehabilitation tool or as an assistive device for spinal cord injured (SCI)...
BACKGROUND: The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning...
This article provides data for five different neuropsychiatric disorders-Attention Deficit Hyperactivity Disorder, Alzheimer's Disease, Autism Spectru...
Recent technological advances in machine learning offer the possibility of decoding complex datasets and discern latent patterns. In this study, we ad...
This study examined the treatment efficacy of proximal-emphasized robotic rehabilitation by using the InMotion ARM (P-IMT) versus distal-emphasized ro...
OBJECTIVE: Lipid metabolism has been implicated in autoimmune disorders, but its relationship with anti-N-methyl-D-aspartate receptor (anti-NMDAR) enc...
OBJECTIVE: Limitations of the manual scoring of polysomnograms, which include data from electroencephalogram (EEG), electro-oculogram (EOG), electroca...
Event-related potentials (ERPs) show promise to be objective indicators of cognitive functioning. The aim of the study was to examine if ERPs recorded...
BACKGROUND: The novel molecule endocan, which is released by endothelium and is regulated by proangiogenic and proinflammatory cytokines, may have a r...
OBJECTIVE: This work proposes a machine-learning based system for a scalp EEG that flags an alarm in advance of a clinical seizure onset.
Needle electromyography can be used to detect the number of changes and morphological changes in motor unit potentials of patients with axonal neurop...
Classification of electroencephalogram (EEG) signal is important in mental decoding for brain-computer interfaces (BCI). We introduced a feature extra...
Health and a vibrant life are sought by everyone. To improve quality of life (QOL), maintain a healthy state, and prevent various diseases, evaluation...
Discriminative methods commonly produce models with relatively good generalization abilities. However, this advantage is challenged in real-world appl...
UNLABELLED: Abstact Purpose: To provide an overview of the feasibility and outcomes of robotic-assisted upper extremity training for individuals with ...