@article{2671,
  keywords = {Classification, Machine Learning, Support Vector Machine, Dyslexia, Electroencephalography},
  author = {Harshani Perera and Mohd Fairuz Shiratuddin and Kok Wai Wong and Kelly Fullarton},
  title = {EEG Signal Analysis of Writing and Typing between Adults with Dyslexia and Normal Controls},
  abstract = {EEG 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.},
  year = {2018},
  journal = {International Journal of Interactive Multimedia and Artificial Intelligence},
  volume = {5},
  number = {1},
  pages = {62-67},
  month = {06/2018},
  issn = {1989-1660},
  url = {http://www.ijimai.org/journal/sites/default/files/files/2018/04/ijimai_5_1_8_pdf_17747.pdf},
  doi = {10.9781/ijimai.2018.04.005},
}