ArticleScientific reports2024
Power spectral density-based resting-state EEG classification of first-episode psychosis.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Schizophrenia: temporal imprecision in phase, time, and frequency domains.Schizophrenia (Heidelberg, Germany) · 2026Article
- Configurable Modular EEG Classification Framework with Multiscale Features and Ensemble Learning: A Reproducible Evaluation for Schizophrenia Detection.Bioengineering (Basel, Switzerland) · 2026Article
- Multiscale Cosine Convolution Neural Network for Robust and Interpretable Epileptic EEG Detection.Biosensors · 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
- Mapping EEG Metrics to Human Affective and Cognitive Models: An Interdisciplinary Scoping Review from a Cognitive Neuroscience Perspective.Biomimetics (Basel, Switzerland) · 2025Review
- Resting-State Electroencephalogram (EEG) as a Biomarker of Learning Disabilities in Children-A Systematic Review.Journal of clinical medicine · 2025Review
- MentalAId: an improved DenseNet model to assist scalable psychosis assessment.BMC psychiatry · 2025Article
- A review on computer-aided diagnostic system to classify the disorders of the gastrointestinal tract.European journal of medical research · 2025Review
- Electrophysiological biomarkers based on CISANET characterize illness severity and suicidal ideation among patients with major depressive disorder.Medical & biological engineering & computing · 2025Article
- Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals.Diagnostics (Basel, Switzerland) · 2025Article
- Enhancing anesthetic techniques for improving whisker stimulation response in the barrel cortex.PloS one · 2025Article
- Deep multimodal representations and classification of first-episode psychosis via live face processing.Frontiers in psychiatry · 2025Article
- Exploring Task-Related EEG for Cross-Subject Early Alzheimer's Disease Susceptibility Prediction in Middle-Aged Adults Using Multitaper Spectral Analysis.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
5 authors.
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Abstract
Historically, the analysis of stimulus-dependent time-frequency patterns has been the cornerstone of most electroencephalography (EEG) studies. The abnormal oscillations in high-frequency waves associated with psychotic disorders during sensory and cognitive tasks have been studied many times. However, any significant dissimilarity in the resting-state low-frequency bands is yet to be established. Spectral analysis of the alpha and delta band waves shows the effectiveness of stimulus-independent EEG in identifying the abnormal activity patterns of pathological brains. A generalized model incorporating multiple frequency bands should be more efficient in associating potential EEG biomarkers with first-episode psychosis (FEP), leading to an accurate diagnosis. We explore multiple machine-learning methods, including random-forest, support vector machine, and Gaussian process classifier (GPC), to demonstrate the practicality of resting-state power spectral density (PSD) to distinguish patients of FEP from healthy controls. A comprehensive discussion of our preprocessing methods for PSD analysis and a detailed comparison of different models are included in this paper. The GPC model outperforms the other models with a specificity of 95.78% to show that PSD can be used as an effective feature extraction technique for analyzing and classifying resting-state EEG signals of psychiatric disorders.
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