Design and Selection of Features under ERP for Correlating and Classifying between Brain Areas and Dyslexia via Machine Learning

We develop a method that is based on processing gathered Event Related Potentials (ERP) signals and the use of machine learning technique for multivariate analysis (i.e. classification) that we apply in order to analyze the differences between Dyslexic and Skilled readers.No human intervention is ne...

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Bibliographic Details
Main Authors: Frid, Alex, Manevitz, Larry M.
Format: Conference Proceeding
Language:English
Subjects:
Classification of Event Related Potentials (ERP)
Decoding
Dyslexia classification
Electrodes
Electroencephalography
Feature extraction
Feature Selection
Machine learning
Visualization
Wavelet transforms
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Summary:We develop a method that is based on processing gathered Event Related Potentials (ERP) signals and the use of machine learning technique for multivariate analysis (i.e. classification) that we apply in order to analyze the differences between Dyslexic and Skilled readers.No human intervention is needed in the analysis process. This is the state of the art results for automatic identification of Dyslexic readers using a Lexical Decision Task. We use mathematical and machine learning based techniques to automatically discover novel complex features that (i) allow for reliable distinction between Dyslexic and Normal Control Skilled readers and (ii) to validate the assumption that most of the differences between Dyslexic and Skilled readers are located in the left hemisphere.Interestingly, these tools also pointed to the fact that High Pass signals (typically considered as "noise" during ERP/EEG analyses) in fact contain significant relevant information.Finally, the proposed scheme can be used for analysis of any ERP based studies.
ISSN:2161-4407
DOI:10.1109/IJCNN48605.2020.9207715