Advancing Arabic Inner Speech Recognition with Machine Learning and Deep Learning
Aya E. Ahmad, Habiba Hafrag, Ziad A. Meligy, Heba Ali Abdelbary, Sahar Selim · 2025
Individuals with motor and communication impairments struggle to access communication tools and fully participate in society. Advances in Brain-Computer Interface (BCI) technology enable direct brain communication via noninvasive EEG signals from inner speech. However, accessible applications for Arabic-speaking individuals remain limited. Our research explores various feature extraction and classification methods on the publicly available Arabic EEG dataset, "ArEEG." Using EEGNet on raw EEG data, we improved cross-validation accuracy by 2%, reaching 27.1% per subject. Additionally, we propose a novel K-Nearest Neighbors (KNN) approach with Relative Wavelet Energy (RWE) and Gabor Transform features, selected via ANOVA, achieving 31.03% accuracy in subject-dependent analysis.