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Found 3 papers, 1 papers with code
EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia
The purpose of this work is to check whether these differences exist and how they are related to children's performance in different language and cognitive tasks commonly used to detect dyslexia.
Deep Learning in current Neuroimaging: a multivariate approach with power and type I error control but arguable generalization ability
We found in the permutation test that CV and RUB methods offer a false positive rate close to the significance level and an acceptable statistical power (although lower using cross-validation).
Ensemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and Other Tasks
Employing class-balancing to build a training set, we trained 3$\times$300 modified CNNs in an ensemble model to classify fMRI connectivity matrices with overall AUROCs of 0. 6774, 0. 7680, and 0. 9222 for ASD vs TD, gender, and task vs rest, respectively.
