Features and Machine Learning for Correlating and Classifying between Brain Areas and Dyslexia
This provides an automatic tool for diagnosing dyslexia, which could aid clinicians and researchers, though it is incremental as it builds on existing ERP analysis methods.
The researchers tackled the problem of automatically identifying dyslexic readers by analyzing Event Related Potentials (ERP) signals with machine learning, achieving state-of-the-art results in classification and validating that differences are primarily located in the left hemisphere.
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.