Machine learning classification of dyslexic children based on EEG local network features
Abstract Machine learning can be used to find meaningful patterns characterizing individual
differences. Deploying a machine learning classifier fed by local features derived from graph
analysis of electroencephalographic (EEG) data, we aimed at designing a neurobiologically-
based classifier to differentiate two groups of children, one group with and the other group
without dyslexia, in a robust way. We used EEG resting-state data of 29 dyslexics and 15
typical readers in grade 3, and calculated weighted connectivity matrices for multiple …
differences. Deploying a machine learning classifier fed by local features derived from graph
analysis of electroencephalographic (EEG) data, we aimed at designing a neurobiologically-
based classifier to differentiate two groups of children, one group with and the other group
without dyslexia, in a robust way. We used EEG resting-state data of 29 dyslexics and 15
typical readers in grade 3, and calculated weighted connectivity matrices for multiple …
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