ArticleFrontiers in psychiatry2021
Identification of Major Psychiatric Disorders From Resting-State Electroencephalography Using a Machine Learning Approach.
Article in Frontiers in psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 3 of them syntheses that pooled it.
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Who cites it
30 citing papers in PubMed, 3 syntheses or guidelines pooled it, 89 citations in OpenAlex.
- Stress and alcohol impact network states involved in emotional processing: relevance of comorbid AUD and psychiatric illnesses.Psychopharmacology · 2026Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder.Frontiers in human neuroscience · 2023Pooled it
- Resting-state EEG default mode network connectivity and suicidal ideation in depressive disorders: a retrospective cross-sectional study.BMC psychiatry · 2026Article
- Article
- Configurable Modular EEG Classification Framework with Multiscale Features and Ensemble Learning: A Reproducible Evaluation for Schizophrenia Detection.Bioengineering (Basel, Switzerland) · 2026Article
- Quantitative electroencephalography as a potential neurophysiological diagnostic biomarker of schizophrenia and first-episode psychosis: a systematic review of clinical implications.Frontiers in psychiatry · 2026Review
- A novel deep learning model for objective quantification of generalized anxiety disorder severity using EEG functional connectivity.Frontiers in psychiatry · 2026Article
- EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation.Schizophrenia bulletin · 2025Article
- Multichannel convolutional transformer for detecting mental disorders using electroancephalogrpahy records.Scientific reports · 2025Article
- Quantitative EEG Insights Into A Hundred Adult ADHD Patients: A Deep Dive Into Test of Variables of Attention (TOVA) Correlations and Attention Dynamics.CNS neuroscience & therapeutics · 2025Article
- Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals.Diagnostics (Basel, Switzerland) · 2025Article
- Decentralized EEG-based detection of major depressive disorder via transformer architectures and split learning.Frontiers in computational neuroscience · 2025Article
- Advances in Electroencephalography for Post-Traumatic Stress Disorder Identification: A Scoping Review.IEEE open journal of engineering in medicine and biology · 2025Article
- Abnormal nonlinear features of EEG microstate sequence in obsessive-compulsive disorder.BMC psychiatry · 2024Article
- Psychiatric disorders from EEG signals through deep learning models.IBRO neuroscience reports · 2024Article
- Revisiting the role of computational neuroimaging in the era of integrative neuroscience.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Machine-Learning-Based Depression Detection Model from Electroencephalograph (EEG) Data Obtained by Consumer-Grade EEG Device.Brain sciences · 2024Article
- Multi-modal EEG NEO-FFI with Trained Attention Layer (MENTAL) for mental disorder prediction.Brain informatics · 2024Article
- Integrating EEG and Ensemble Learning for Accurate Grading and Quantification of Generalized Anxiety Disorder: A Novel Diagnostic Approach.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We aimed to develop a machine learning (ML) classifier to detect and compare major psychiatric disorders using electroencephalography (EEG). We retrospectively collected data from medical records, intelligence quotient (IQ) scores from psychological assessments, and quantitative EEG (QEEG) at resting-state assessments from 945 subjects [850 patients with major psychiatric disorders (six large-categorical and nine specific disorders) and 95 healthy controls (HCs)]. A combination of QEEG parameters including power spectrum density (PSD) and functional connectivity (FC) at frequency bands was used to establish models for the binary classification between patients with each disorder and HCs. The support vector machine, random forest, and elastic net ML methods were applied, and prediction performances were compared. The elastic net model with IQ adjustment showed the highest accuracy. The best feature combinations and classification accuracies for discrimination between patients and HCs with adjusted IQ were as follows: schizophrenia = alpha PSD, 93.83%; trauma and stress-related disorders = beta FC, 91.21%; anxiety disorders = whole band PSD, 91.03%; mood disorders = theta FC, 89.26%; addictive disorders = theta PSD, 85.66%; and obsessive-compulsive disorder = gamma FC, 74.52%. Our findings suggest that ML in EEG may predict major psychiatric disorders and provide an objective index of psychiatric disorders.
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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.