A brief introduction to magnetoencephalography (MEG) and its clinical applications
Magnetoencephalography (MEG) plays a pivotal role in the diagnosis of brain disorders. In
this review, we have investigated potential MEG applications for analysing brain disorders …
this review, we have investigated potential MEG applications for analysing brain disorders …
Converging robotic technologies in targeted neural rehabilitation: a review of emerging solutions and challenges
Recent advances in the field of neural rehabilitation, facilitated through technological
innovation and improved neurophysiological knowledge of impaired motor control, have …
innovation and improved neurophysiological knowledge of impaired motor control, have …
Deep learning for automated epileptiform discharge detection from scalp EEG: A systematic review
Automated interictal epileptiform discharge (IED) detection has been widely studied, with
machine learning methods at the forefront in recent years. As computational resources …
machine learning methods at the forefront in recent years. As computational resources …
Deep learning for robust detection of interictal epileptiform discharges
D Geng, A Alkhachroum, MAM Bicchi… - Journal of neural …, 2021 - iopscience.iop.org
Objective. Automatic detection of interictal epileptiform discharges (IEDs, short as' spikes')
from an epileptic brain can help predict seizure recurrence and support the diagnosis of …
from an epileptic brain can help predict seizure recurrence and support the diagnosis of …
Automated detection of epileptic seizures using multiscale and refined composite multiscale dispersion entropy
M Chakraborty, D Mitra - Chaos, Solitons & Fractals, 2021 - Elsevier
Epilepsy is one of the most common neurological disorders. The electroencephalogram
(EEG) is a valuable tool for the detection of epileptic seizures. The diagnosis of epilepsy …
(EEG) is a valuable tool for the detection of epileptic seizures. The diagnosis of epilepsy …
Computer-assisted EEG diagnostic review for idiopathic generalized epilepsy
Epilepsy diagnosis can be costly, time-consuming, and not uncommonly inaccurate. The
reference standard diagnostic monitoring is continuous video-electroencephalography …
reference standard diagnostic monitoring is continuous video-electroencephalography …
A novel channel selection method for BCI classification using dynamic channel relevance
Brain-Computer Interface (BCI) provides a direct communicating pathway between the
human brain and the external environment. In the BCI systems, electroencephalography …
human brain and the external environment. In the BCI systems, electroencephalography …
IENet: a robust convolutional neural network for EEG based brain-computer interfaces
Y Du, J Liu - Journal of neural engineering, 2022 - iopscience.iop.org
Objective. Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) develop
into novel application areas with more complex scenarios, which put forward higher …
into novel application areas with more complex scenarios, which put forward higher …
Automated detection of abnormalities from an EEG recording of epilepsy patients with a compact convolutional neural network
Electroencephalography (EEG) is essential for the diagnosis of epilepsy, but it requires
expertise and experience to identify abnormalities. It is thus crucial to develop automated …
expertise and experience to identify abnormalities. It is thus crucial to develop automated …
A deep learning framework with multi-perspective fusion for interictal epileptiform discharges detection in scalp electroencephalogram
Objective. Interictal epileptiform discharges (IEDs) are an important and widely accepted
biomarker used in the diagnosis of epilepsy based on scalp electroencephalography (EEG) …
biomarker used in the diagnosis of epilepsy based on scalp electroencephalography (EEG) …