Latest AI and machine learning research in neurology for healthcare professionals.
The study of brain networks by resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method for identifying patients with dementia from healthy controls (HC). Using graph theory, different aspects of the brain network can be efficiently characterized by calculating measures of integration and segregation. In this study, we combined a graph theoretical approach with advanced ...
Aims Labyrinthectomized rats are suitable models to test consequences of vestibular lesion and are widely used to study neural plasticity. We describe a combined microsurgical-chemical technique that can be routinely performed with minimum damage. Methods Caudal leaflet of the parotis is elevated. The tendinous fascia covering the bulla is opened frontally from the sternomastoid muscle's tendon wh...
Apoptosis plays a role in the process of tissue damage after myocardial infarction (MI). This study was designed to investigate the possible effect of...
OBJECTIVES: To determine the effect of early systemic administration of IL-10 on peripheral neuropathic pain induced by tibial nerve permanent transec...
UNLABELLED: Robot therapy promotes functional recovery after spinal cord injury (SCI) in animal and clinical studies. Trunk actions are important in a...
Brain-computer interface (BCI) systems provide an alternative communication and control approach for people with limited motor function. Therefore, th...
This study proposes a Cross-Correlated Delay Shift (CCDS) supervised learning rule to train neurons with associated spatiotemporal patterns to classif...
In this work, EEG spectral features of different subjects are uniquely mapped into a 2D feature space. Such distinctive 2D features pave the way to id...
Electroencephalographic (EEG)-based emotion recognition has attracted increasing attention from the field of human-computer interaction (HCI). However...
Several studies have found evidence for corticolimbic Theta electroencephalographic (EEG) oscillation in the neural processing of visual stimuli perce...
Robotic devices are a promising and dynamic tool in the realm of post-stroke rehabilitation. Researchers are still investigating how the use of robots...
This paper presents an electroencephalography (EEG) based-classification of between pre- and post-mental load tasks for mental fatigue detection from ...
Following stroke, rehabilitation exercises paired with medical treatment are important for the recovery and maintenance of upper limb function. We dev...
Stroke is one of the leading causes of disability worldwide. Consequently, many stroke survivors exhibit difficulties undergoing voluntary movement in...
Reaching and grasping are two of the most affected functions after stroke. Hybrid rehabilitation systems combining Functional Electrical Stimulation w...
Tremor is a common symptom shared in both Parkinson's disease (PD) and Essential tremor (ET) subjects. The differential diagnosis of PD and ET tremor ...
The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing ...
Monitoring driver's intentions beforehand is an ambitious aim, which will bring a huge impact on the society by preventing traffic accidents. Hence, i...
We analyze brain waves acquired through a consumer-grade EEG device to investigate its capabilities for user identification and authentication. First,...
The ability of correlation integral for automatic seizure detection using scalp EEG data has been re-examined in this paper. To facilitate the detecti...