Aug 2026· Brain Topography· Vol 39· 0 citations· 49 references
Medicine
TL;DR
The proposed electroencephalography-based ASD detection method was evaluated against previous approaches using the same datasets, showing a substantial improvement in performance, and the need to evaluate the proposed approach on larger datasets to further ensure model generalization.
This study delivers a technically rigorous, transparent, and automated pipeline for electrophysiological signal characterization, establishing a preliminary computational foundation for neurophysiological differentiation in pediatric ASD.
Kazi Mahatir Mohammad Samir, A. Sarker, Sawrav Das· The FASEB Journal· 0 citations
A comparative temporal deep learning framework for EEG-based ASD classification by evaluating principal component analysis (PCA) and multiscale principal component analysis (MS-PCA) as feature representations combined with a recurrent neural network with bidirectional long short-term memory (RNN-BiLSTM) and a temporal...
M. Muliyadi, Melinda, Yuwaldi Away et al.· Journal of Electronics Elect...· 0 citations
It is shown that subject-level validation, preprocessing, and architectural decisions materially influence EEG-based ADHD classification, and EEG-MSCNet contributes both a model and a systematic evaluation framework.
Javier Sanchis, T. Kechadi, Miguel A. Teruel et al.· Multidimensional systems and...· 0 citations
Findings indicate that the learned brain network representations capture distinct and quantifiable abnormalities in ASD-related neural dynamics, establishing the proposed framework as a robust, interpretable, and measurement-aligned tool for objective ASD assessment.
Madhuparna Das, Poulomi Pal, M. Mahadevappa· IEEE Transactions on Instrum...· 0 citations
These findings demonstrate that integrating multiresolution time-frequency analysis with HVG-based graph-theoretic feature extraction provides an accurate, interpretable, and computationally efficient framework for objective EEG-based ASD screening.
Using a portable EEG device with a low participant burden and a deep learning model to distinguish between the typical development group (TD) and the NDD group, comprising children with ASD, ADHD, and ASD + ADHD is used.
