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 …
Deep learning models for diagnosis of schizophrenia using EEG signals: emerging trends, challenges, and prospects
Schizophrenia (ScZ) is a chronic neuropsychiatric disorder characterized by disruptions in
cognitive, perceptual, social, emotional, and behavioral functions. In the traditional …
cognitive, perceptual, social, emotional, and behavioral functions. In the traditional …
Automatic identification of schizophrenia based on EEG signals using dynamic functional connectivity analysis and 3D convolutional neural network
M Shen, P Wen, B Song, Y Li - Computers in Biology and Medicine, 2023 - Elsevier
Schizophrenia (ScZ) is a devastating mental disorder of the human brain that causes a
serious impact of emotional inclinations, quality of personal and social life and healthcare …
serious impact of emotional inclinations, quality of personal and social life and healthcare …
A customized ECA-CRNN model for emotion recognition based on EEG signals
Y Song, Y Yin, P Xu - Electronics, 2023 - mdpi.com
Electroencephalogram (EEG) signals are electrical signals generated by changes in brain
potential. As a significant physiological signal, EEG signals have been applied in various …
potential. As a significant physiological signal, EEG signals have been applied in various …
An efficient automated detection of schizophrenia using k-NN and bag of words features
Converging shreds of evidence from several research argue that the aberrations present in
a brain's Structural Magnetic Resonance Imaging (sMRI) are the leading cause of …
a brain's Structural Magnetic Resonance Imaging (sMRI) are the leading cause of …
Fractal dimensions and machine learning for detection of Parkinson's disease in resting-state electroencephalography
Parkinson's disease (PD) is an incurable neurological disorder that degenerates the
cerebrospinal nervous system and hinders motor functions. Electroencephalography (EEG) …
cerebrospinal nervous system and hinders motor functions. Electroencephalography (EEG) …
A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning
The electroencephalogram (EEG) serves as an essential tool in exploring brain activity and
holds particular importance in the field of mental health research. This review paper …
holds particular importance in the field of mental health research. This review paper …
[HTML][HTML] An empirical wavelet transform-based approach for motion artifact removal in electroencephalogram signals
AB Nayak, A Shah, S Maheshwari, V Anand… - Decision Analytics …, 2024 - Elsevier
Motion artifacts reduce the quality of information in the electroencephalogram (EEG) signals.
In this study, we have developed an effective approach to mitigate the motion artifacts in …
In this study, we have developed an effective approach to mitigate the motion artifacts in …
Scoping Review of Deep Learning Techniques for Diagnosis, Drug Discovery, and Vaccine Development in Leishmaniasis
Leishmania, a single‐cell parasite prevalent in tropical and subtropical regions worldwide,
can cause varying degrees of leishmaniasis, ranging from self‐limiting skin lesions to …
can cause varying degrees of leishmaniasis, ranging from self‐limiting skin lesions to …
EEG signal-based classification of mental tasks using a one-dimensional ConvResT model
The classification of mental or cognitive tasks in real time using single-or multi-channel EEG
signals is an important field of research for neurofeedback and portable brain–computer …
signals is an important field of research for neurofeedback and portable brain–computer …