The search for a dyslexia diagnosis based on exclusively objective methods is currently a challenging task. Usually, this disorder is
analyzed by means of behavioral tests prone to errors due to their subjective nature; e.g. the subject’s mood while doing the test can affect the results. Understanding the brain processes involved is key to proportionate a correct analysis and avoid these types of problems. It is in this task, biomarkers like electroencephalograms can help to obtain an objective measurement of the brain behavior that can be used to perform several analyses and ultimately making a diagnosis, keeping the human interaction at minimum. In this work, we used recorded electroencephalograms of children with and without dyslexia while a sound stimulus is played.
We aim to detect whether there are significant differences in adaptation when the same stimulus is applied at different times. Our results show that following this process, a machine learning pipeline can be built with AUC values up to 0.73.
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