# Fusion techniques of time frequency-based images to predict the outcome of rTMS depression therapy

arXiv: https://arxiv.org/abs/2610.00380 · HTML: https://mlsanity.com/paper/2610.00380/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 43 references that are in the map.
- First posted on arXiv 30 Sep 2026.

## Builds on (most cited first, 2 of 2)

- [Brain Tumor Classification in MRI Images: A Computationally Efficient Convolutional Neural Network](https://mlsanity.com/paper/2605.12560/) (2026)
- [Identifying Ketamine Responses in Treatment-Resistant Depression Using a Wearable Forehead EEG](https://mlsanity.com/paper/1805.11446/) (2018)

## Abstract (arXiv)

Depression is a mental condition that can lead to suicide and self-harm. Predicting the outcome of depression treatment is one of the most difficult tasks for clinicians. Among various treatment options, repetitive Transcranial Magnetic Stimulation (rTMS) is a widely used non-invasive method. Predicting rTMS response using Electroencephalogram (EEG) data is difficult because of high inter-subject variability and limited features from single-domain analysis. We introduce two fusion techniques, montage and blending, to overcome these limitations and extract richer features from EEG-derived Time-Frequency (TF) images. We then propose a lightweight custom Convolutional Neural Network (CNN) trained on fused TF representations. textcolorblackWe use a primary dataset of 15 patients and a secondary dataset of 46 patients. We run two sets of experiments. The first set uses segment-level 10-fold cross-validation. In this setup segments from the same patient can appear in both training and testing. The Montage CWTST fusion reaches 99.90% accuracy on the primary dataset and 91.90% on the secondary dataset. The second set uses strict subject-disjoint cross-validation. All segments of a patient stay in one fold and no patient appears in both training and testing. Performance collapses. We test four time-frequency methods, six fusion mechanisms, and fourteen model architectures. With one exception, every configuration on both cohorts falls between AUC 0.31 and 0.54 and every 95% confidence interval contains 0.5. A patient-level permutation test on the best standalone method returns p = 0.703. The best subject-level result is Montage CWTST on the primary cohort, which reaches AUC 0.874 ± 0.183 and 82.7% accuracy.