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May 22, 2026CNS Neuroscience & Therapeutics3 citationsOpen Access

Stability‐Driven Selection of EEG Connectivity Features for Psychosis Classification: A Network‐Based Machine Learning Approach

MNMahdi NaeimUniversity of Mohaghegh Ardabili MNMohammad NarimaniUniversity of Mohaghegh Ardabili

Key Points

The aim is to develop a machine learning framework for classifying psychosis using EEG connectivity features and identifying stable markers.
Cross-sectional analysis using EEG data from 43 participants (19 psychosis, 24 controls)
Extracted functional connectivity measures and used SVM and RF models for classification
Applied nested cross-validation with subject-wise splitting to evaluate feature importance and stability scores.
SVM model achieved an accuracy of 91.2% and an AUC of 0.967.
Identified theta-band connectivity features, notably fronto-parietal PLV and global efficiency, as reliable markers.
SHAP analysis confirmed consistent contributions of these features across subjects.

Abstract

OBJECTIVES: To develop a network-based machine learning framework for classifying psychosis using EEG connectivity features and to identify stable, reproducible candidate markers through a stability-driven approach. METHODS: This study was designed as a cross-sectional analytical secondary data analysis using a publicly available EEG dataset comprising 43 participants (19 psychosis, 24 controls). Functional connectivity measures (PLV and coherence) and graph-theoretical network features were extracted across frequency bands. A nested cross-validation framework with subject-wise splitting was applied. Feature importance was evaluated using permutation importance and SHAP, and stability scores were computed across folds to identify robust features. Classification was performed using support vector machine (SVM) and random forest (RF) models. RESULTS: The SVM model achieved superior performance (accuracy: 91.2%, AUC: 0.967). Stability analysis identified theta-band connectivity features, particularly fronto-parietal PLV and global efficiency, as the most reliable and discriminative. SHAP analysis confirmed their consistent contribution across subjects. However, these findings should be interpreted as exploratory given the relatively small sample size and the use of a single dataset. CONCLUSION: Stable connectivity-derived network features provide interpretable and robust candidate EEG markers for psychosis classification. The proposed framework enhances reproducibility and supports the potential of EEG-based tools for clinical screening, while emphasizing the need for external validation.

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