[HTML][HTML] A review on functional near-infrared spectroscopy and application in stroke rehabilitation

C Huo, G Xu, W Li, H Xie, T Zhang, Y Liu, Z Li - Medicine in Novel …, 2021 - Elsevier
Functional near-infrared spectroscopy (fNIRS) has gained great interest as a noninvasive
modality to study the changes in cerebral hemodynamics related to brain activity. The …

Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG

M Moghaddari, MZ Lighvan, S Danishvar - Computer Methods and …, 2020 - Elsevier
Abstract Background and objective Attention-Deficit/Hyperactivity Disorder (ADHD) is a
chronic behavioral disorder in children. Children with ADHD face many difficulties in …

A deep learning framework for identifying children with ADHD using an EEG-based brain network

H Chen, Y Song, X Li - Neurocomputing, 2019 - Elsevier
The convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm.
However, the application of DL techniques in attention-deficit/hyperactivity disorder (ADHD) …

Computer aided diagnosis system using deep convolutional neural networks for ADHD subtypes

A Ahmadi, M Kashefi, H Shahrokhi… - … Signal Processing and …, 2021 - Elsevier
Background Attention deficit hyperactivity disorder (ADHD) is a ubiquitous
neurodevelopmental disorder affecting many children. Therefore, automated diagnosis of …

A systematic review on feature extraction in electroencephalography-based diagnostics and therapy in attention deficit hyperactivity disorder

P Arpaia, A Covino, L Cristaldi, M Frosolone… - Sensors, 2022 - mdpi.com
A systematic review on electroencephalographic (EEG)-based feature extraction strategies
to diagnosis and therapy of attention deficit hyperactivity disorder (ADHD) in children is …

Standard multiscale entropy reflects neural dynamics at mismatched temporal scales: What's signal irregularity got to do with it?

JQ Kosciessa, NA Kloosterman… - PLoS computational …, 2020 - journals.plos.org
Multiscale Entropy (MSE) is used to characterize the temporal irregularity of neural time
series patterns. Due to its' presumed sensitivity to non-linear signal characteristics, MSE is …

Diagnosis of attention deficit hyperactivity disorder using non‐linear analysis of the EEG signal

YK Boroujeni, AA Rastegari… - IET systems biology, 2019 - Wiley Online Library
Attention deficit hyperactivity disorder (ADHD) is a common behavioural disorder that may
be found in 5%–8% of the children. Early diagnosis of ADHD is crucial for treating the …

Attention Deficit Hyperactivity Disorder Diagnosis using non-linear univariate and multivariate EEG measurements: a preliminary study

M Rezaeezadeh, S Shamekhi, M Shamsi - Physical and engineering …, 2020 - Springer
Abstract Attention Deficit Hyperactivity Disorder (ADHD) is a common neuro-developmental
disorder of childhood. In this study we propose two classification algorithms for …

Combining functional near-infrared spectroscopy and EEG measurements for the diagnosis of attention-deficit hyperactivity disorder

A Güven, M Altınkaynak, N Dolu, M İzzetoğlu… - Neural Computing and …, 2020 - Springer
Recently multimodal neuroimaging which combines signals from different brain modalities
has started to be considered as a potential to improve the accuracy of diagnosis. The current …

Identification of attention-deficit hyperactivity disorder based on the complexity and symmetricity of pupil diameter

S Nobukawa, A Shirama, T Takahashi, T Takeda… - Scientific Reports, 2021 - nature.com
Adult attention-deficit/hyperactivity disorder (ADHD) frequently leads to psychological/social
dysfunction if unaddressed. Identifying a reliable biomarker would assist the diagnosis of …