A Deep-Learning-Powered Pipeline for EEG-Based Dyslexia Detection Achieving ~94.66% Accuracy

Dyslexia is a language-based learning disorder that significantly impacts reading, spelling, and writing abilities. Early identification of dyslexia is critical for targeted intervention and long-term academic success. Recent research has explored electroencephalography (EEG) signals to capture the neurophysiological correlates of dyslexia, often revealing distinctive patterns in brain oscillations and event-related potentials. In this study, we present a deep-learning-based approach for EEG-based dyslexia detection that achieves 94.66% classification accuracy on a real-world dataset. Our methodology encompasses preprocessing, feature selection via mutual information, hyperparameter tuning using KerasTuner, and final network training with advanced regularization and callbacks. Notably, we benchmark our pipeline against classical dyslexia detection workflows, highlighting how our approach leverages modern techniques (feature normalization, batch normalization, dropout, automated hyperparameter optimization) to reach high accuracy while remaining computationally tractable. We also discuss potential translational applications in educational and clinical settings, challenges of EEG data variability, and the need for large-scale standardized EEG repositories for robust, replicable dyslexia detection research.