What question did this study set out to answer?

The aim is to create an effective EEG-based method for detecting dyslexia in children using deep learning techniques.

March 29, 2026Frontiers in Neuroinformatics1 citationsOpen Access

Discrete wavelet transform-driven optimized deep learning-based framework for dyslexia detection using EEG signals

TJTabassum Gull Jan SKSajad Mohammad Khan SBSajid Yousuf Bhat

Key Points

The aim is to create an effective EEG-based method for detecting dyslexia in children using deep learning techniques.
Acquired EEG recordings from 51 children during cognitive tasks.
Processed and decomposed EEG signals into frequency bands using discrete wavelet transform.
Applied filter-based feature selection to optimize feature sets for classification.
Evaluated deep neural networks alongside classical machine learning models for performance comparison.
The compact deep neural network achieved a classification accuracy of 98.85%.
Outperformed all classical machine learning baselines, raw-EEG deep learning models, and existing pipelines.
Demonstrated the effectiveness of DWT-driven EEG analysis combined with deep learning for dyslexia detection.

Abstract

Purpose Dyslexia is a prevalent neurodevelopmental disorder that impairs a children’s ability to reading, writing, and language processing despite normal cognitive skills. Early identification is vital for timely support and interventions in children with dyslexia. This study aimed to develop an efficient EEG-based pipeline for dyslexia detection using deep learning techniques, while providing a consistent evaluation protocol for fair comparison across models and prior approaches. Methods EEG recordings were acquired from 51 participants (26: dyslexic and 25: non-dyslexic), aged 5–10 years, during cognitive task performance. These signals were processed, segmented, and decomposed into standard frequency bands (alpha, beta, delta, and theta) using the discrete wavelet transform to capture discriminative neural patterns. Filter-based feature selection techniques were applied before classification to optimize performance and reduce redundancy to identify the most informative features. These ranked and individual band-wise features were systematically evaluated with classical machine learning baselines (Decision Trees, SVM, k-NN, and ensemble learners) alongside the proposed deep neural networks. In addition, we benchmarked end-to-end raw-EEG deep learning baselines (1D-CNN, LSTM, and EEGNet) and re-implemented representative existing pipelines, all evaluated on our dataset using the same evaluation protocol. Results The proposed compact deep neural network with four hidden layers achieved the best performance, reaching classification accuracy of 98.85%, outperforming all baseline models, raw-EEG deep learning baselines, and re-implemented approaches. Conclusion These findings support the feasibility of DWT-driven EEG analysis combined with deep learning for more accurate and early dyslexia detection. The proposed approach holds promise as a non-invasive screening tool to support improved educational outcomes through early diagnosis and targeted intervention.

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