Search research articles

Related Concept Videos

Seizures: Classification

Seizures: Classification

Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same journal

Hierarchical disruptions of white matter rich-club organization in comorbid insomnia with anxiety: a diffusion tensor imaging study.

Psychiatry research. Neuroimaging·2026

Same journal

The amygdala in bipolar disorder: A cross-sectional study examining the relevance of structural alterations of amygdala subfields for cognitive functioning in patients with bipolar disorder.

Psychiatry research. Neuroimaging·2026

Same journal

Combined transformer encoder-CNN architecture with texture features for MRI-based Alzheimer's disease detection.

Psychiatry research. Neuroimaging·2026

Same journal

Changes in EEG-derived entropy are associated with symptom improvement in major depressive disorder following electroconvulsive therapy.

Psychiatry research. Neuroimaging·2026

Same journal

P50 sensory gating in children with ADHD, autism spectrum disorder, and neurotypicals: Within-session test-retest reliability and evaluation of methodologies.

Psychiatry research. Neuroimaging·2026

Same journal

Effects of riluzole on excitation-inhibition imbalance and functional connectivity in 22q11.2 deletion syndrome: a multimodal 7-Tesla MRI study.

Psychiatry research. Neuroimaging·2026

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 29, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

EEG-based schizophrenia classification using attention-integrated deep convolutional networks.

Anjali Sagar Jangde¹, Gyanendra Kumar Verma¹

¹National Institute of Technology, Information Technology, Raipur, Chhattisgarh, India.

Psychiatry Research. Neuroimaging

|January 13, 2026

Summary

This summary is machine-generated.

A new deep learning model using convolutional attention networks can automatically detect schizophrenia from electroencephalography (EEG) signals. This AI approach shows high accuracy on one dataset, highlighting potential for improved schizophrenia diagnosis.

Keywords:

Attention mechanism Convolutional neural networks (CNN)Electroencephalogram (EEG)Schizophrenia (SZ)

More Related Videos

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Jun 29, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Neuroscience
Artificial Intelligence
Biomedical Engineering

Background:

Schizophrenia is a complex psychiatric disorder.
Electroencephalography (EEG) offers a non-invasive method for biomarker identification in schizophrenia.

Purpose of the Study:

To develop and evaluate a deep learning framework for automated schizophrenia detection using EEG signals.
To integrate spatial and temporal feature extraction for enhanced diagnostic accuracy.

Main Methods:

A convolutional attention-based deep learning framework was proposed.
The model utilizes convolutional layers for spatial feature extraction and an attention mechanism for temporal pattern focus.
Experiments were conducted on the Moscow EEG and IBIB PAN datasets.

Main Results:

The model achieved 73.98% accuracy on the Moscow EEG dataset, potentially due to demographic and recording limitations.
A classification accuracy of 98.45% was obtained on the IBIB PAN dataset, indicating strong performance.
The study highlights the model's discriminative capability and generalization potential.

Conclusions:

Attention-augmented convolutional networks show promise for schizophrenia detection from EEG.
Dataset variability (demographic, acquisition) presents challenges for model generalization.
Further research is needed to refine models for diverse populations and recording conditions.