A novel approach to dyslexia detection: combining EEG data and cognitive tasks with advanced machine learning.

Existing dyslexia detection methods typically rely on either EEG or screening tests. This study introduces a multimodal two-stage methodology for dyslexia detection that integrates both EEG signals and screening test data, using spectral features (Shannon entropy and Power Spectral Density) across various frequency bands. The novelty of the proposed approach stems in collecting firsthand paired EEG recordings and screening data from patients, and combining them via two-stage framework, which improves predictive accuracy and yields a comprehensive neurological assessment. Several machine learning models were trained and, upon evaluation, the Random Forest classifier performed best, achieving 98.36% accuracy, 99.2% precision, and 97.63% recall using only EEG data. When both EEG and screening data were combined, performance improved to 98.69% accuracy, 98.90% precision, and 98.90% recall, significantly outperforming existing methodologies, with results validated via statistical tests. Dyslexic individuals exhibited higher delta and theta energy, particularly in the central, parietal, and temporal regions, indicating deficits in phonological processing and working memory. Lower Shannon entropy values in dyslexics suggest increased randomness in neural activity and reduced processing ability. Feature importance analysis identified delta, theta, and gamma energies as the most discriminative features. This study highlights the effectiveness of multimodal data fusion in enhancing dyslexia detection, improving early diagnosis, and facilitating interventions for children aged 5-9 years. These results underscore the potential of advanced spectral feature extraction and robust machine learning in dyslexia diagnostics, setting a benchmark in the field, with future work focusing on validating these results using larger datasets and exploring additional predictive features.