Clustering natural language morphemes from EEG signals using the Artificial Bee Colony algorithm

Clustering natural language morphemes from EEG signals using the Artificial Bee Colony algorithm

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 Artif...

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Bibliographic Details
Main Authors: Sulaiman, Suriani, Ahmed Yahya, Saba, Mohd Shukor, Nur Sakinah, Ismail , Amelia Ritahani, Zaahirah, Qazi, Yaacob, Hamwira Sakti, Abdul Rahman, Abdul Wahab, Dzulkifli, Mariam Adawiah
Format: Book Chapter
Language:English
Published: Switzerland 2015
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://irep.iium.edu.my/49579/1/Clustering_Natural_Language_Morphemes_from_EEG_Signals_using_ABC.pdf
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Summary: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.

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