• DocumentCode
    690565
  • Title

    Early Identification of Dyslexic Preschoolers Based on Neurophysiological Signals

  • Author

    Karim, I. ; Qayoom, Abdul ; Wahab, Abdul ; Kamaruddin, Norhaslinda

  • Author_Institution
    Dept. of Comput. Sci., Int. Islamic Univ. Malaysia, Kuala Lumpur, Malaysia
  • fYear
    2013
  • fDate
    23-24 Dec. 2013
  • Firstpage
    362
  • Lastpage
    366
  • Abstract
    Dyslexia is a learning difficulty and in most cases cannot be identified until a child is already in the third grade or later. At this time a dyslexic child have only an one-in-seven chance of ever catching up with his or her peers in reading, writing, speaking or listening. Early identification can pave the way for early intervention and the dyslexic child can be helped at an early stage. Furthermore, the results yielded are the best when the intervention in the form of providing specialized instructions or carried out through some other way yields best results when done at preschoolers. Thus the importance of early identification. The following study is devoted to the EEG based identification of dyslexia for preschool going children. In this analysis feature extraction are carried out using KDE and MLP is used for classification of the features extracted. The results show promising classification accuracy.
  • Keywords
    electroencephalography; feature extraction; medical signal processing; neurophysiology; paediatrics; signal classification; EEG based identification; KDE; Kernel density estimation; MLP; dyslexic child; dyslexic preschooler identification; feature extraction; learning difficulty; multilayer perceptron; neurophysiological signals; signal classification; Accuracy; Biological neural networks; Computers; Electrodes; Electroencephalography; Feature extraction; Kernel; Dyslexia; EEG; KDE; MLP; Presecreening;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Computer Science Applications and Technologies (ACSAT), 2013 International Conference on
  • Conference_Location
    Kuching
  • Type

    conf

  • DOI
    10.1109/ACSAT.2013.78
  • Filename
    6836607