Validation-Aware Retrospective EEG Treatment-Response Modelling Using Chaotic Pattern of Prime Numbers Features: Segment-Level Separability and Subject-Wise Generalisation

Overview

1. Department of Information Science, University of North Texas, Denton, TX 76203, USA; (H.A.); (M.M.)
2. Department of Psychiatry, University of Tehran, Tehran 141556619, Iran; (R.R.); (R.K.)
3. School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK

Abstract

This study presents a validation-aware EEG framework based on Chaotic Pattern of Prime Numbers (CPPN) features for depression treatment-response modelling across one SSRI cohort and two rTMS cohorts. CPPN features were evaluated through a seven-protocol validation hierarchy spanning random segment splitting, segment-level cross-validation, nested segment-level cross-validation, leave-N-subjects-out, fixed-feature leave-one-subject-out (LOSO), nested leave-N-subjects-out, and nested LOSO, with normalisation, NCA ranking, feature-count selection where applicable, and model fitting confined to the appropriate training partitions. In the representative K-nearest neighbour (KNN) comparison, segment-level 10-fold CV achieved accuracies of 98.79% for Mumtaz SSRI, 99.32% for small Atieh rTMS, and 99.42% for big Atieh rTMS, demonstrating strong discriminative structure in the CPPN feature space. In the available segment-level KNN comparison, CPPN features with fold-internal NCA-selected feature sets exceeded conventional statistical EEG features by 29.53, 16.33, and 11.45 percentage points across the three cohorts. Subject-wise validation produced lower and more cohort-dependent estimates, with the best fixed-feature LOSO accuracy of 80.00% and the best nested LOSO accuracy of 73.33% in the small Atieh rTMS cohort. These results show that CPPN provides a compact, inspectable and computationally accessible EEG feature representation, while the validation hierarchy gives a transparent account of how performance changes from segment-level separability to held-out-subject evaluation. The main contribution is methodological: this study combines an original CPPN feature representation with explicit validation-depth analysis, leakage-aware feature selection, and interpretable channel/bin inspection. It therefore provides a rigorous basis for future externally validated EEG treatment-response studies without claiming prospective clinical deployment from the present retrospective cohorts.

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

Code

The paper says that its authors' code is available on request: it was not published with the paper, so there is nothing to verify.

The paper's code and data availability statement is in the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none (its code is available on request), so it has no map.

Data

Datasets cited

• figshare:2, at figshare; found in the references
• figshare:4244171, at figshare; found in “Data Availability Statement”

Data Availability Statement

The Mumtaz SSRI dataset analysed in this study is publicly available from the source described in the original dataset publication [10,24] and through Figshare at https://doi.org/10.6084/m9.figshare.4244171.v2. The Atieh Hospital rTMS datasets are not publicly available because they contain clinical participant data and are subject to institutional and ethical restrictions. Requests for access to the private rTMS data and derived analysis materials may be directed to the corresponding author and will be considered subject to internal approval, institutional data-governance requirements, and participant-confidentiality restrictions where appropriate. Derived CPPN feature matrices, class labels, and analysis scripts may be made available by the corresponding author upon reasonable request, subject to institutional approvals and applicable data-governance restrictions.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 14 keywords, 29 references.

Cite

This paper

Akbari, H., Mete, M., Rostami, R., Kazemi, R., & Sadiq, M. T. (2026). Validation-Aware Retrospective EEG Treatment-Response Modelling Using Chaotic Pattern of Prime Numbers Features: Segment-Level Separability and Subject-Wise Generalisation. Bioengineering (Basel, Switzerland), 13(6), 659.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.3390/brainsci15090977

Predicting Depression Therapy Outcomes Using EEG-Derived Amplitude Polar Maps.

Journal: Brain sciences

In common: figshare 4244171, depression, other, EEG, 1 other category, 18 references

[2] doi:10.3390/brainsci16030301

Towards the Development of a Deep Learning Framework Using Adaptive and Non-Adaptive Time-Frequency Features for EEG-Based Depression Therapy Prediction.

Journal: Brain sciences

In common: figshare 4244171, depression, EEG, clinical / translational, 14 references

[3] doi:10.1038/s41598-025-02452-7

Automated depression detection via cloud based EEG analysis with transfer learning and synchrosqueezed wavelet transform.

Journal: Scientific reports

In common: figshare 4244171, depression, EEG, 4 references

[4] doi:10.3390/s24216815

Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis.

Journal: Sensors (Basel, Switzerland)

In common: figshare 4244171, depression, EEG, clinical / translational, 4 references

[5] doi:10.3390/s25103178

Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods.

Journal: Sensors (Basel, Switzerland)

In common: figshare 2, figshare 4244171, EEG

[6] doi:10.3390/s26051417

Depression Detection from Three-Channel Resting-State EEG Using a Hybrid Conv1D and Spectral-Statistical Fusion Model.

Journal: Sensors (Basel, Switzerland)

In common: figshare 2, depression, EEG, 2 references

[7] doi:10.3390/diagnostics15020210

Depression Detection and Diagnosis Based on Electroencephalogram (EEG) Analysis: A Systematic Review.

Journal: Diagnostics (Basel, Switzerland)

In common: figshare 4244171, depression, EEG, clinical / translational, 2 references

[8] doi:10.1049/htl2.70084

Machine Learning-Based Depression Recognition With Preserved Efficacy From Compressed EEG Signals Using Wavelet Transform and Adaptive Filtering.

Journal: Healthcare technology letters

In common: figshare 4244171, depression, EEG, clinical / translational, 1 reference

[9] doi:10.3390/bioengineering12020095

TSF-MDD: A Deep Learning Approach for Electroencephalography-Based Diagnosis of Major Depressive Disorder with Temporal-Spatial-Frequency Feature Fusion.

Journal: Bioengineering (Basel, Switzerland)

In common: figshare 4244171, depression, EEG, clinical / translational, 1 reference

[10] doi:10.3390/bioengineering13040410

An Effective and Interpretable EEG-Based Depression Recognition Method Using Hybrid Feature Selection.

Journal: Bioengineering (Basel, Switzerland)

In common: figshare 4244171, depression, EEG, 1 reference

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion: code errors and mismatches between the code and the paper about this paper and its code, from readers and its authors, beside the paper and its tracing map.

Reproductions: reports from readers who ran the authors' code and did not get the paper's result, what they ran, with which environment, commit and data.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.