| Autor: |
Harshani Perera, Mohd Fairuz Shiratuddin, Kok Wai Wong, Kelly Fullarton |
| Jazyk: |
angličtina |
| Rok vydání: |
2018 |
| Předmět: |
|
| Zdroj: |
International Journal of Interactive Multimedia and Artificial Intelligence, Vol 5, Iss 1, Pp 62-67 (2018) |
| Druh dokumentu: |
article |
| ISSN: |
1989-1660 |
| DOI: |
10.9781/ijimai.2018.519 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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