  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.
