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Machine learning Classification of Dyslexic Children based on EEG Local Network Features

Z. Rezvani, M. Zare, G. Žarić, M. Bonte, J. Tijms, M.W. Van der Molen, G. Fraga González
doi: https://doi.org/10.1101/569996
Z. Rezvani
1Institute for Cognitive and Brain Sciences, Shahid Beheshti University G.C., Tehran, Iran
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M. Zare
2School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
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G. Žarić
3Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Netherlands
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M. Bonte
3Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Netherlands
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J. Tijms
4Rudolfs Berlin Center, Amsterdam, Netherlands
5IWAL Institute, Amsterdam, Netherlands
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M.W. Van der Molen
6Department of Psychology, University of Amsterdam, Netherlands
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G. Fraga González
7Amsterdam Brain and Cognition, University of Amsterdam, Netherlands
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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 frequency bands using the phase lag index (PLI). From the connectivity matrices, we derived weighted connectivity graphs. A number of local network measures were computed from those graphs, and 37 False Discovery Rate (FDR) corrected features were selected as input to a Support Vector Machine (SVM) and a common K Nearest Neighbors (KNN) classifier. Cross validation was employed to assess the machine-learning performance and random shuffling to assure the performance appropriateness of the classifier and avoid features overfitting. The best performance was for the SVM using a polynomial kernel. Children were classified with 95% accuracy based on local network features from different frequency bands. The automatic classification techniques applied to EEG graph measures showed to be both robust and reliable in distinguishing between typical and dyslexic readers.

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Posted March 13, 2019.
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Machine learning Classification of Dyslexic Children based on EEG Local Network Features
Z. Rezvani, M. Zare, G. Žarić, M. Bonte, J. Tijms, M.W. Van der Molen, G. Fraga González
bioRxiv 569996; doi: https://doi.org/10.1101/569996
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Machine learning Classification of Dyslexic Children based on EEG Local Network Features
Z. Rezvani, M. Zare, G. Žarić, M. Bonte, J. Tijms, M.W. Van der Molen, G. Fraga González
bioRxiv 569996; doi: https://doi.org/10.1101/569996

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