SynthesisFrontiers in human neuroscience2024
A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning.
Synthesis in Frontiers in human neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Decoding Upper-Limb Motor Imagery from EEG Signals: A Systematic Review of Methods and Applications.Annals of biomedical engineering · 2026Pooled it
- Atieh Schizophrenia EEG, a novel high-quality dataset designed to advance biomarker and machine learning research.Scientific data · 2026Article
- Differential quadruple pattern: A new EEG signal classification framework.IBRO neuroscience reports · 2026Article
- Quantitative Electroencephalography as a Complement to Symptom-Based Psychiatric Diagnosis: A Narrative Review.Journal of personalized medicine · 2026Review
- Stability-Driven Selection of EEG Connectivity Features for Psychosis Classification: A Network-Based Machine Learning Approach.CNS neuroscience & therapeutics · 2026Article
- Configurable Modular EEG Classification Framework with Multiscale Features and Ensemble Learning: A Reproducible Evaluation for Schizophrenia Detection.Bioengineering (Basel, Switzerland) · 2026Article
- MDEFusion: A Multi-Domain EEG Feature Fusion Network With Bidirectional Attention LSTM and Half-Split Crossover SAE for Schizophrenia Recognition.Brain and behavior · 2026Article
- Electroencephalography-Based Machine and Deep Learning Approaches for the Diagnosis of Dissociative Disorders: A Comprehensive Review.Biological psychiatry global open science · 2026Review
- Correlation of frontal lobe electroencephalogram asymmetry and cognitive prognosis in schizophrenia.World journal of psychiatry · 2026Article
- Classifying Obsessive-Compulsive Disorder from Resting-State EEG Using Convolutional Neural Networks: A Pilot Study.Computational psychiatry (Cambridge, Mass.) · 2026Article
- An optimized EEG-based hybrid deep learning framework for schizophrenia detection.Biomedical engineering letters · 2026Article
- Evaluating the Severity of Autism Spectrum Disorder from EEG: A Multidisciplinary Approach Using Statistical and Artificial Intelligence Frameworks.Bioengineering (Basel, Switzerland) · 2025Article
- Schizophrenia Detection Using Convolutional Neural Networks on EEG Data.Research square · 2025Article
- Classifying Obsessive-Compulsive Disorder from Resting-State EEG using Convolutional Neural Networks: A Pilot Study.medRxiv : the preprint server for health sciences · 2025Article
- Deep learning approach for automatic assessment of schizophrenia and bipolar disorder in patients using R-R intervals.PLoS computational biology · 2025Article
- An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion.Scientific reports · 2025Article
- SchizoLMNet: a modified lightweight MobileNetV2- architecture for automated schizophrenia detection using EEG-derived spectrograms.Physical and engineering sciences in medicine · 2025Article
- A Machine-Learning-Based Analysis of Resting State Electroencephalogram Signals to Identify Latent Schizotypal and Bipolar Development in Healthy University Students.Diagnostics (Basel, Switzerland) · 2025Article
- Ensemble learning techniques reveals multidimensional EEG feature alterations in pediatric schizophrenia.Frontiers in human neuroscience · 2025Article
- An EEG-based machine learning framework for diagnosing acute sleep deprivation.Frontiers in physiology · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 examines the application of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), for classifying schizophrenia (SCZ) through EEG. It includes a thorough literature review that addresses the difficulties, methodologies, and discoveries in this field. ML approaches utilize conventional models like Support Vector Machines and Decision Trees, which are interpretable and effective with smaller data sets. In contrast, DL techniques, which use neural networks such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), are more adaptable to intricate EEG patterns but require significant data and computational power. Both ML and DL face challenges concerning data quality and ethical issues. This paper underscores the importance of integrating various techniques to enhance schizophrenia diagnosis and highlights AI's potential role in this process. It also acknowledges the necessity for collaborative and ethically informed approaches in the automated classification of SCZ using AI.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.