Eeg signal processing for medical diagnosis, healthcare, and monitoring: A comprehensive review
EEG is a common and safe test that uses small electrodes to record electrical signals from
the brain. It has a broad range of applications in medical diagnosis, including diagnosis of …
the brain. It has a broad range of applications in medical diagnosis, including diagnosis of …
[HTML][HTML] Adazd-Net: Automated adaptive and explainable Alzheimer's disease detection system using EEG signals
Background: Alzheimer's disease (AZD) is a degenerative neurological condition that
causes dementia and leads the brain to atrophy. Although AZD cannot be cured, early …
causes dementia and leads the brain to atrophy. Although AZD cannot be cured, early …
Empowering precision medicine: AI-driven schizophrenia diagnosis via EEG signals: A comprehensive review from 2002–2023
Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional,
and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of …
and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of …
EEG-based graph neural network classification of Alzheimer's disease: An empirical evaluation of functional connectivity methods
Alzheimer's disease (AD) is the leading form of dementia worldwide. AD disrupts neuronal
pathways and thus is commonly viewed as a network disorder. Many studies demonstrate …
pathways and thus is commonly viewed as a network disorder. Many studies demonstrate …
Alzheimer's diseases diagnosis using fusion of high informative BiLSTM and CNN features of EEG signal
M Imani - Biomedical Signal Processing and Control, 2023 - Elsevier
Electroencephalography (EEG) signals are low cost and available data for diagnosis of
mental disorders such as Alzheimer's diseases (AD). Each EEG signal contains information …
mental disorders such as Alzheimer's diseases (AD). Each EEG signal contains information …
Spatial–temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram
Functional connectivity of the human brain, representing statistical dependence of
information flow between cortical regions, significantly contributes to the study of the intrinsic …
information flow between cortical regions, significantly contributes to the study of the intrinsic …
Low-cost EEG multi-subject recording platform for the assessment of students' attention and the estimation of academic performance in secondary school
VJ Fuentes-Martinez, S Romero, MA Lopez-Gordo… - Sensors, 2023 - mdpi.com
The level of student attention in class greatly affects their academic performance. Teachers
typically rely on visual inspection to react to students' attention in time, but this subjective …
typically rely on visual inspection to react to students' attention in time, but this subjective …
Diagnosis of autism spectrum disorder based on functional brain networks and machine learning
Autism is a multifaceted neurodevelopmental condition whose accurate diagnosis may be
challenging because the associated symptoms and severity vary considerably. The wrong …
challenging because the associated symptoms and severity vary considerably. The wrong …
Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia
Objective. Schizophrenia (SCZ) is a severe mental disorder associated with persistent or
recurrent psychosis, hallucinations, delusions, and thought disorders that affect …
recurrent psychosis, hallucinations, delusions, and thought disorders that affect …
Automated schizophrenia detection model using blood sample scattergram images and local binary pattern
The main goal of this paper is to advance the field of automated Schizophrenia (SZ)
detection methods by presenting a pioneering feature engineering technique that achieves …
detection methods by presenting a pioneering feature engineering technique that achieves …