Evaluating the performance of EEG based on ANN to predict the effectiveness of tDCS combined evaluative conditioning on obsession symptoms reduction in contamination OCD patients: Secondary analysis of data from a randomized controlled trial.

Overview

1. Rayan Research Center for Neuroscience & Behavior, Department of Biology, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran
2. Departmen of Biomedical Engineering, Faculty of Electrical Engineering, Islamic Azad University of Mashhad, Mashhad, Iran
3. School of Community and Global Health, Claremont Graduate University, Claremont, United States of America
4. Department of Psychology, Faculty of Education and Psychology, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

Over 40% of Obsessive-Compulsive Disorder (OCD) patients do not respond to common treatments. This study was a secondary analysis of data from a randomized controlled trial, for predictability of effectiveness of Transcranial Direct Current Stimulation (tDCS) with Contamination-Based OCD (C-OCD) using artificial neural networks (ANN) and electroencephalography (EEG) signals. Out of 54 C-OCD patients, 48 were randomized into 4 groups. Using a 2 × 2 factorial design, each group received a combined intervention (real or sham) based on tDCS and Disgust Reduction Evaluative Conditioning (DREC) in 10 sessions. Evaluations included the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) and EEG recordings at rest (eyes open). Among the various features extracted from EEG (Fuzzy Synchronization Likelihood (FSL), Power Spectrum, and Recurrence Quantification Analysis (RQA), the Relief algorithm identified appropriate features based on intervention effectiveness for each frequency band and group allocation. The obsessive symptoms reduction was predicted using appropriate features, the fully connected feedforward network and Radial Basis Functions (RBF) networks. The ANN inputs were the appropriate features extracted from the EEG signal before the interventions. To predict the effectiveness level, the Y-BOCS change score (pre- to post-intervention) was given to the models as the desired output. Both networks had the best results for the first three proposed Relief features. The fully connected feedforward network optimized by Gray Wolf Optimizer (GWO) achieved the best predictive performance with an average RMSE 0.57 ± 0.4. Based on the calculated error and comparison with Y-BOCS change intervals, EEG data from C-OCD patients combined with ANN effectively predicted the extent to which each type of intervention can change patients’ Y-BOCS score. So, the therapist can decide whether to perform or select each type of intervention for the patient before starting treatment. Our findings confirm the feasibility of using pre-intervention EEG signals combined with ANN to predict individual responses in C-OCD patients.

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Version 2, 28 September 2026

• Funding: added Cognitive Sciences and Technologies Council: 11939; Ferdowsi University of Mashhad: 11939, 3/57073

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 10 MeSH terms, 47 references.

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This paper

Asadollahzadeh Shamkhal, F., Moghimi, A., Kobravi, H. R., Salehi Fadardi, J., & Raeis Al Mohaddesin, F. (2026). Evaluating the performance of EEG based on ANN to predict the effectiveness of tDCS combined evaluative conditioning on obsession symptoms reduction in contamination OCD patients: Secondary analysis of data from a randomized controlled trial. PloS one, 21(8), e0354614. https://doi.org/10.1371/journal.pone.0354614

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