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SOM-Based Class Discovery for Emotion Detection Based on DEAP Dataset

SOM-Based Class Discovery for Emotion Detection Based on DEAP Dataset

Aladdin Ayesh, Miguel Arevalillo-Herra´ez, Pablo Arnau-González
Copyright: © 2018 |Volume: 10 |Issue: 1 |Pages: 12
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781522544029|DOI: 10.4018/IJSSCI.2018010102
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MLA

Ayesh, Aladdin, et al. "SOM-Based Class Discovery for Emotion Detection Based on DEAP Dataset." IJSSCI vol.10, no.1 2018: pp.15-26. http://doi.org/10.4018/IJSSCI.2018010102

APA

Ayesh, A., Arevalillo-Herra´ez, M., & Arnau-González, P. (2018). SOM-Based Class Discovery for Emotion Detection Based on DEAP Dataset. International Journal of Software Science and Computational Intelligence (IJSSCI), 10(1), 15-26. http://doi.org/10.4018/IJSSCI.2018010102

Chicago

Ayesh, Aladdin, Miguel Arevalillo-Herra´ez, and Pablo Arnau-González. "SOM-Based Class Discovery for Emotion Detection Based on DEAP Dataset," International Journal of Software Science and Computational Intelligence (IJSSCI) 10, no.1: 15-26. http://doi.org/10.4018/IJSSCI.2018010102

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Abstract

This paper investigates the possibility of identifying classes by clustering. This study includes employing Self-Organizing Maps (SOM) in identifying clusters from EEG signals that could then be mapped to emotional classes. Beginning by training varying sizes of SOM with the EEG data provided from the public dataset: DEAP. The produced graphs showing Neighbor Distance, Sample Hits, and Weight Position are examined. Following that, the ground-truth label provided in DEAP is tested, in order to identify correlations between the label and the clusters produced by the SOM. The results show that there is a potential of class discovery using SOM-based clustering. It is then concluded that by evaluating the implications of this work and the difficulties in evaluating its outcome.

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