01639nas a2200253   4500000000100000000000100001008004100002260001200043653001900055653002100074653002700095653001300122653002700135100002000162700002800182700001700210700002000227245009500247856009500342300001000437490000600447520091800453022001401371       2018                            d  c06/201810aClassification10aMachine Learning10aSupport Vector Machine10aDyslexia10aElectroencephalography1 aHarshani Perera1 aMohd Fairuz Shiratuddin1 aKok Wai Wong1 aKelly Fullarton00aEEG Signal Analysis of Writing and Typing between Adults with Dyslexia and Normal Controls  uhttp://www.ijimai.org/journal/sites/default/files/files/2018/04/ijimai_5_1_8_pdf_17747.pdf  a62-670 v53 aEEG is one of the most useful techniques used to represent behaviours of the brain and helps explore valuable insights through the measurement of brain electrical activity. Hence, plays a vital role in detecting neurological conditions. In this paper, we identify some unique EEG patterns pertaining to dyslexia, which is a learning disability with a neurological origin. Although EEG signals hold important insights of brain behaviours, uncovering these insights are not always straightforward due to its complexity. We tackle this using machine learning and uncover unique EEG signals generated in adults with dyslexia during writing and typing as well as optimal EEG electrodes and brain regions for classification. This study revealed that the greater level of difficulties seen in individuals with dyslexia during writing and typing compared to normal controls are reflected in the brainwave signal patterns.  a1989-1660