Changes in EEG complexity with neurofeedback and multi-sensory learning in children with dyslexia: a multiscale entropy analysis

Official URL: http://dx.doi.org/10.1080/21622965.2020.1772794

Multiscale entropy analysis (MSE) is a novel entropy-based approach for measuring dynamical complexity in physiological systems over a range of temporal scales. MSE has been successfully applied in the literature when measuring autism traits, Alzheimer's, and schizophrenia. However, until now, there has been no research on MSE applied to children with dyslexia. In this study, we have applied MSE analysis to the EEG data of an experimental group consisting of children with dyslexia as well as a control group consisting of typically developing children and compared the results. The experimental group comprised 16 participants with dyslexia who visited Ankara University Medical Faculty Child Neurology Department, and the control group comprised 20 age-matched typically developing children with no reading or writing problems. MSE was calculated for one continuous 60-s epoch for each experimental and control group's EEG session data. The experimental group showed significantly lower complexity at the lowest temporal scale and the medium temporal scales than the typically developing group. Moreover, the experimental group received 60 neurofeedback and multi-sensory learning sessions, each lasting 30 min, with Auto Train Brain. Post-treatment, the experimental group's lower complexity increased to the typically developing group's levels at lower and medium temporal scales in all channels.