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

Overview

1. Department of Psychology, Faculty of Educational Sciences and Psychology University of Mohaghegh Ardabili Ardabil Iran

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.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• doi:10.18112/openneuro.ds004000.v1.0.0, at OpenNeuro; found in the acknowledgements
• openneuro:ds004000, at OpenNeuro; found in “Data Availability Statement”

Data Availability Statement

The data supporting the findings of this study are openly available in the OpenNeuro repository under the accession number ds004000 at the following link: https://openneuro.org/datasets/ds004000/versions/1.0.0. This dataset, entitled Fribourg Ultimatum Game in Schizophrenia Study, contains task‐based EEG recordings from 43 participants (19 patients with psychosis and 24 healthy controls) (19). All data were collected at the University of Fribourg, Switzerland, in compliance with ethical approval (Ref: 054/13‐CER‐FR) and are shared in the BIDS format. The analyses presented in this manuscript were conducted as a secondary data analysis using this publicly available dataset.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 5 keywords, 12 MeSH terms, 12 references.

Cite

This paper

Naeim, M., & Narimani, M. (2026). Stability-Driven Selection of EEG Connectivity Features for Psychosis Classification: A Network-Based Machine Learning Approach. CNS neuroscience & therapeutics, 32(5), e70943. https://doi.org/10.1002/cns.70943

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