Features and Machine Learning for Correlating and Classifying between Brain Areas and Dyslexia

TL;DR

This paper introduces a machine learning-based method for analyzing ERP signals to automatically distinguish between dyslexic and skilled readers, revealing novel features and hemisphere-specific differences.

Contribution

The study presents a fully automated approach using mathematical and machine learning techniques to identify features that differentiate dyslexic from skilled readers in ERP data.

Findings

High Pass signals contain significant relevant information.

Reliable distinction between dyslexic and skilled readers achieved.

Most differences are localized in the left hemisphere.

Abstract

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 the most of the differences between Dyslexic and Skilled readers 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 contains significant relevant information. Finally, the proposed scheme can be used for analysis of any ERP based studies.