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.
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Sulaiman, S. et al. (2015). Clustering Natural Language Morphemes from EEG Signals Using the Artificial Bee Colony Algorithm. In: Phon-Amnuaisuk, S., Au, T. (eds) Computational Intelligence in Information Systems. Advances in Intelligent Systems and Computing, vol 331. Springer, Cham. https://doi.org/10.1007/978-3-319-13153-5_6
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DOI: https://doi.org/10.1007/978-3-319-13153-5_6
Publisher Name: Springer, Cham
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