Vocal tasks-based EEG and speech signal analysis in children with neurodevelopmental disorders: a multimodal...

Neurodevelopmental disorders (NDs) often hamper multiple functional prints of a child brain. Despite several studies on their neural and speech responses, multimodal researches on NDs are extremely rare. The present work examined the electroencephalography (EEG) and speech signals of the ND and control children, who performed "Hindi language" vocal tasks (V) of seven different categories, viz. 'vowel', 'consonant', 'one syllable', 'multi-syllable', 'compound', 'complex', and 'sentence' (V1-V7). Statistical testing of EEG parameters showed substantially high beta and gamma band energies in frontal, central, and temporal head sites of NDs for tasks V1-V5 and in parietal too for V6. For the 'sentence' task (V7), the NDs yielded significantly high theta and low alpha energies in the parietal area. These findings imply that even performing a general context-based task exerts a heavy cognitive loading in neurodevelopmental subjects. They also exhibited poor auditory comprehension while executing a long phrasing. Further, the speech signal analysis manifested significantly high amplitude (for V1-V7) and frequency (for V3-V7) perturbations in the voices of ND children. Moreover, the classification of subjects as ND or control was done via EEG and speech features. We attained 100% accuracy, precision, and F-measure using EEG features of all tasks, and using speech features of the 'complex' task. Jointly, the 'complex' task transpired as the best vocal stimuli among V1-V7 for characterizing ND brains. Meanwhile, we also inspected inter-relations between EEG energies and speech attributes of the ND group. Our work, thus, represents a unique multimodal layout to explore the distinctiveness of neuro-impaired children.