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Clustering Natural Language Morphemes from EEG Signals Using the Artificial Bee Colony Algorithm

  • Suriani Sulaiman
  • Saba Ahmed Yahya
  • Nur Sakinah Mohd Shukor
  • Amelia Ritahani Ismail
  • Qazi Zaahirah
  • Hamwira Yaacob
  • Abdul Wahab Abdul Rahman
  • Mariam Adawiah Dzulkifli
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 331)

Abstract

We present a preliminary study on the use of a Brain Computer Interface (BCI) device to investigate the feasibility of recognizing patterns of natural language morphemes from EEG signals. This study aims at analyzing EEG signals for the purpose of clustering natural language morphemes using the Artificial Bee Colony (ABC) algorithm. Using as input the features extracted from EEG signals during morphological priming tasks, our experimental results indicate that applying the ABC algorithm on EEG datasets to cluster Malay morphemes produces promising results.

Keywords

Clustering Artificial Bee Colony algorithm EEG signals Natural language morphemes Morphological priming tasks BCI 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Suriani Sulaiman
    • 1
  • Saba Ahmed Yahya
    • 1
  • Nur Sakinah Mohd Shukor
    • 1
  • Amelia Ritahani Ismail
    • 1
  • Qazi Zaahirah
    • 1
  • Hamwira Yaacob
    • 1
  • Abdul Wahab Abdul Rahman
    • 1
  • Mariam Adawiah Dzulkifli
    • 2
  1. 1.Department of Computer Science, Kulliyyah of Information and Communication TechnologyInternational Islamic UniversityKuala LumpurMalaysia
  2. 2.Department of Psychology, Kulliyyah of Islamic Revealed Knowledge and Human SciencesInternational Islamic UniversityKuala LumpurMalaysia

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