ArticlePloS one2021
EEG microstate features for schizophrenia classification.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 2 of them syntheses that pooled it.
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Who cites it
51 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning.Frontiers in human neuroscience · 2024Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Immediate and long-term effects of orbitofrontal cortex stimulation on EEG microstates in schizophrenia.Translational psychiatry · 2026Trial
- Dynamic, differentiated clinical trajectories and prediction in clinical high risk for psychosis.Molecular psychiatry · 2026Article
- Robust Multi-Site ADHD Classification via GraphSAGE-Based Functional Connectivity Modeling from rs-fMRI.Bioengineering (Basel, Switzerland) · 2026Article
- Adaptive drift-aware multi-stage deep learning framework for EEG-based schizophrenia diagnosis.BioData mining · 2026Article
- Configurable Modular EEG Classification Framework with Multiscale Features and Ensemble Learning: A Reproducible Evaluation for Schizophrenia Detection.Bioengineering (Basel, Switzerland) · 2026Article
- Fusion of Multi-Paradigm EEG Microstate Features to Enhance the Recognition of Mild Cognitive Impairment.Brain and behavior · 2026Article
- Fusing EEG Features Extracted by Microstate Analysis and Empirical Mode Decomposition for Diagnosis of Schizophrenia.Sensors (Basel, Switzerland) · 2026Article
- State-Dependent CNN-GRU Reinforcement Framework for Robust EEG-Based Sleep Stage Classification.Biomimetics (Basel, Switzerland) · 2026Article
- Article
- Transformer architecture for diagnosing schizophrenia disabilities through EEG analysis.Frontiers in physiology · 2026Article
- Exploring Informative Items for Bipolar Disorder Classification Using Machine Learning With Anger Coping Styles in Combination With the Mood Disorder Questionnaire and Bipolar Spectrum Diagnostic Scale.Psychiatry investigation · 2025Article
- Driver Fatigue Detection using EEG Microstate Features and Support Vector Machines.Journal of biomedical physics & engineering · 2025Article
- Predicting Suicidal Ideation in Community-based Older Adults Using Self-report Questionnaires with Machine Learning.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2025Article
- EEG Resting-state Microstate Dynamics in Children and Adolescents with Avoidant/Restrictive Food Intake Disorder (ARFID).Brain topography · 2025Article
- Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks.Brain sciences · 2025Article
- Microstate Dynamics in Working Memory: Exploring Spatial Information Coding of Stimulus and Behavioral Performance.Brain and behavior · 2025Article
- Moving toward precision and personalized treatment strategies in psychiatry.The international journal of neuropsychopharmacology · 2025Review
- Altered microstate C and D dynamics in high social anxiety: a resting-state EEG study.Frontiers in psychology · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electroencephalography (EEG) microstate analysis is a method wherein spontaneous EEG activity is segmented at sub-second levels to analyze quasi-stable states. In particular, four archetype microstates and their features are known to reflect changes in brain state in neuropsychiatric diseases. However, previous studies have only reported differences in each microstate feature and have not determined whether microstate features are suitable for schizophrenia classification. Therefore, it is necessary to validate microstate features for schizophrenia classification. Nineteen microstate features, including duration, occurrence, and coverage as well as thirty-one conventional EEG features, including statistical, frequency, and temporal characteristics were obtained from resting-state EEG recordings of 14 patients diagnosed with schizophrenia and from 14 healthy (control) subjects. Machine-learning based multivariate analysis was used to evaluate classification performance. EEG recordings of patients and controls showed different microstate features. More importantly, when differentiating among patients and controls, EEG microstate features outperformed conventional EEG ones. The performance of the microstate features exceeded that of conventional EEG, even after optimization using recursive feature elimination. EEG microstate features applied with conventional EEG features also showed better classification performance than conventional EEG features alone. The current study is the first to validate the use of microstate features to discriminate schizophrenia, suggesting that EEG microstate features are useful for schizophrenia classification.
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