{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T23:48:50Z","timestamp":1792453730467,"version":"4.5.0"},"reference-count":38,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T00:00:00Z","timestamp":1780531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/bioengineering13060659","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T01:23:21Z","timestamp":1780622601000},"page":"659","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Validation-Aware Retrospective EEG Treatment-Response Modelling Using Chaotic Pattern of Prime Numbers Features: Segment-Level Separability and Subject-Wise Generalisation"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9746-4981","authenticated-orcid":false,"given":"Hesam","family":"Akbari","sequence":"first","affiliation":[{"name":"Department of Information Science, University of North Texas, Denton, TX 76203, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0600-8073","authenticated-orcid":false,"given":"Mutlu","family":"Mete","sequence":"additional","affiliation":[{"name":"Department of Information Science, University of North Texas, Denton, TX 76203, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9318-108X","authenticated-orcid":false,"given":"Reza","family":"Rostami","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, University of Tehran, Tehran 141556619, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2433-0712","authenticated-orcid":false,"given":"Reza","family":"Kazemi","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, University of Tehran, Tehran 141556619, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7410-5951","authenticated-orcid":false,"given":"Muhammad Tariq","family":"Sadiq","sequence":"additional","affiliation":[{"name":"School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e63","DOI":"10.1017\/S2045796023000756","article-title":"Changing trends in the global burden of mental disorders from 1990 to 2019 and predicted levels in 25 years","volume":"32","author":"Wu","year":"2023","journal-title":"Epidemiol. Psychiatr. Sci."},{"key":"ref_2","unstructured":"World Health Organization (2026, May 17). Depressive Disorder (Depression). Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/depression."},{"key":"ref_3","unstructured":"World Health Organization (2026, May 17). Suicide. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/suicide."},{"key":"ref_4","unstructured":"U.S. Food and Drug Administration (2025, July 10). Selective Serotonin Reuptake Inhibitors (SSRIs) Information, Available online: https:\/\/www.fda.gov\/media\/132665\/download?attachment."},{"key":"ref_5","unstructured":"U.S. Food and Drug Administration (2025, July 10). Repetitive Transcranial Magnetic Stimulation (rTMS) Systems: Class II Special Controls Guidance, Available online: https:\/\/www.fda.gov\/medical-devices\/guidance-documents-medical-devices-and-radiation-emitting-products\/repetitive-transcranial-magnetic-stimulation-rtms-systems-class-ii-special-controls-guidance."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5530","DOI":"10.1038\/s41598-018-23584-z","article-title":"Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables","volume":"8","author":"Iniesta","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Beer, C., Rae, F., Semmler, A., and Voisey, J. (2024). Biomarkers in the Diagnosis and Prediction of Medication Response in Depression and the Role of Nutraceuticals. Int. J. Mol. Sci., 25.","DOI":"10.3390\/ijms25147992"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1139\/jpn.0945","article-title":"Magnetic resonance imaging and prediction of outcome in patients with major depressive disorder","volume":"34","author":"MacQueen","year":"2009","journal-title":"J. Psychiatry Neurosci. JPN"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1017\/S0033291703008377","article-title":"Socio-economic status, family disruption and residential stability in childhood: Relation to onset, recurrence and remission of major depression","volume":"33","author":"Gilman","year":"2003","journal-title":"Psychol. Med."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Mumtaz, W., Xia, L., Mohd Yasin, M.A., Azhar Ali, S.S., and Malik, A.S. (2017). A wavelet-based technique to predict treatment outcome for major depressive disorder. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0171409"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1975","DOI":"10.1016\/j.clinph.2013.04.010","article-title":"A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder","volume":"124","author":"Reilly","year":"2013","journal-title":"Clin. Neurophysiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e1918377","DOI":"10.1001\/jamanetworkopen.2019.18377","article-title":"Use of machine learning for predicting escitalopram treatment outcome from electroencephalography recordings in adult patients with depression","volume":"3","author":"Zhdanov","year":"2020","journal-title":"JAMA Netw. Open"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jaworska, N., De la Salle, S., Ibrahim, M.H., Blier, P., and Knott, V. (2019). Leveraging machine learning approaches for predicting antidepressant treatment response using electroencephalography (EEG) and clinical data. Front. Psychiatry, 9.","DOI":"10.3389\/fpsyt.2018.00768"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e206653","DOI":"10.1001\/jamanetworkopen.2020.6653","article-title":"Evaluation of a machine learning model based on pretreatment symptoms and electroencephalographic features to predict outcomes of antidepressant treatment in adults with depression: A prespecified secondary analysis of a randomized clinical trial","volume":"3","author":"Rajpurkar","year":"2020","journal-title":"JAMA Netw. Open"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1038\/s41587-019-0397-3","article-title":"An electroencephalographic signature predicts antidepressant response in major depression","volume":"38","author":"Wu","year":"2020","journal-title":"Nat. Biotechnol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"946","DOI":"10.1016\/j.bbe.2021.06.006","article-title":"Prediction of drug response in major depressive disorder using ensemble of transfer learning with convolutional neural network based on EEG","volume":"41","author":"Shahabi","year":"2021","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"10147","DOI":"10.1038\/s41598-023-35545-2","article-title":"A convolutional recurrent neural network with attention for response prediction to repetitive transcranial magnetic stimulation in major depressive disorder","volume":"13","author":"Shahabi","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.clinph.2024.09.002","article-title":"Using deep learning and pretreatment EEG to predict response to sertraline, bupropion, and placebo","volume":"167","author":"Ravan","year":"2024","journal-title":"Clin. Neurophysiol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"120424","DOI":"10.1016\/j.jad.2025.120424","article-title":"Neurophysiological mechanisms and predictive modeling of SSRI treatment response in depression disorder based on multidimensional EEG features","volume":"393","author":"Li","year":"2025","journal-title":"J. Affect. Disord."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Brookshire, G., Kasper, J., Blauch, N.M., Wu, Y.C., Glatt, R., Merrill, D.A., Gerrol, S., Yoder, K.J., Quirk, C., and Lucero, C. (2024). Data leakage in deep learning studies of translational EEG. Front. Neurosci., 18.","DOI":"10.3389\/fnins.2024.1373515"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"110608","DOI":"10.1016\/j.compbiomed.2025.110608","article-title":"The role of data partitioning on the performance of EEG-based deep learning models in supervised cross-subject analysis: A preliminary study","volume":"196","author":"Zanola","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Singh, A.K., and Krishnan, S. (2023). Trends in EEG signal feature extraction applications. Front. Artif. Intell., 5.","DOI":"10.3389\/frai.2022.1072801"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1109\/TCDS.2020.3040438","article-title":"A matrix determinant feature extraction approach for decoding motor and mental imagery EEG in subject-specific tasks","volume":"14","author":"Sadiq","year":"2020","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_24","unstructured":"Mumtaz, W. (2025, March 01). MDD Patients and Healthy Controls EEG Data (New). Dataset. Available online: https:\/\/figshare.com\/articles\/dataset\/EEG_Data_New\/4244171\/2."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","article-title":"EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis","volume":"134","author":"Delorme","year":"2004","journal-title":"J. Neurosci. Methods"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s10916-014-0131-0","article-title":"The effect of multiscale PCA de-noising in epileptic seizure detection","volume":"38","author":"Kevric","year":"2014","journal-title":"J. Med. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xie, S., and Krishnan, S. (2011). Signal decomposition by multi-scale PCA and its applications to long-term EEG signal classification. Proceedings of the The 2011 IEEE\/ICME International Conference on Complex Medical Engineering, IEEE.","DOI":"10.1109\/ICCME.2011.5876798"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"161","DOI":"10.4304\/jcp.7.1.161-168","article-title":"Neighborhood component feature selection for high-dimensional data","volume":"7","author":"Yang","year":"2012","journal-title":"J. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6562","DOI":"10.1073\/pnas.102102699","article-title":"Selection bias in gene extraction on the basis of microarray gene-expression data","volume":"99","author":"Ambroise","year":"2002","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Varma, S., and Simon, R. (2006). Bias in error estimation when using cross-validation for model selection. BMC Bioinform., 7.","DOI":"10.1186\/1471-2105-7-91"},{"key":"ref_31","first-page":"2079","article-title":"On over-fitting in model selection and subsequent selection bias in performance evaluation","volume":"11","author":"Cawley","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_32","unstructured":"Lundberg, S.M., and Lee, S.I. (2017). A unified approach to interpreting model predictions. NIPS\u201917: Proceedings of the 31st International Conference on Neural Information Processing Systems, Curran Associates Inc."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016). \u201cWhy Should I Trust You?\u201d: Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery.","DOI":"10.1145\/2939672.2939778"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1109\/TCDS.2022.3207350","article-title":"Prediction of Treatment Outcome in Major Depressive Disorder using Ensemble of Hybrid Transfer Learning and Long Short Term Memory based on EEG Signal","volume":"15","author":"Shahabi","year":"2023","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1007\/s13246-024-01392-2","article-title":"Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal","volume":"47","author":"Mirjebreili","year":"2024","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"104822","DOI":"10.1016\/j.bspc.2023.104822","article-title":"Prediction of treatment outcome for repetitive transcranial magnetic stimulation in major depressive disorder using connectivity measures and ensemble of pre-trained deep learning models","volume":"85","author":"Shahabi","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2350007","DOI":"10.1142\/S0129065723500077","article-title":"Attention-based convolutional recurrent deep neural networks for the prediction of response to repetitive transcranial magnetic stimulation for major depressive disorder","volume":"33","author":"Shahabi","year":"2023","journal-title":"Int. J. Neural Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1007\/s11571-022-09881-4","article-title":"Prediction of response to repetitive transcranial magnetic stimulation for major depressive disorder using hybrid Convolutional recurrent neural networks and raw Electroencephalogram Signal","volume":"17","author":"Shahabi","year":"2023","journal-title":"Cogn. Neurodyn."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/6\/659\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T02:25:41Z","timestamp":1780626341000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/6\/659"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,4]]},"references-count":38,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["bioengineering13060659"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13060659","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,4]]}}}