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dc.contributor.authorPerera, Perera, Harshani Harshani
dc.contributor.authorShiratuddin, Mohd Fairuz
dc.contributor.authorWong, Kok Wai
dc.contributor.authorFullarton, Kelly
dc.date2018-06
dc.date.accessioned2022-01-27T08:36:05Z
dc.date.available2022-01-27T08:36:05Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/12365
dc.description.abstractEEG 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.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)es_ES
dc.relation.ispartofseries;vol. 5, nº 1
dc.relation.urihttps://ijimai.org/journal/bibcite/reference/2671es_ES
dc.rightsopenAccesses_ES
dc.subjectclassificationes_ES
dc.subjectmachine learninges_ES
dc.subjectsupport vector machinees_ES
dc.subjectdyslexiaes_ES
dc.subjectelectroencephalographyes_ES
dc.subjectIJIMAIes_ES
dc.titleEEG Signal Analysis of Writing and Typing between Adults with Dyslexia and Normal Controlses_ES
dc.typearticlees_ES
reunir.tag~IJIMAIes_ES
dc.identifier.doihttp://doi.org/10.9781/ijimai.2018.04.005


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