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

Aladdin Ayesh; Miguel Arevalillo-Herra´ez; Pablo Arnau-González

doi:10.4018/ijssci.2018010102

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

Aladdin Ayesh^* (Corresponding Author), Miguel Arevalillo-Herra´ez, Pablo Arnau-González

^*Corresponding author for this work

Computing Science

De Montfort University

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	2
Number of pages	12
Journal	International Journal of Software Science and Computational Intelligence
Volume	10
Issue number	1
DOIs	https://doi.org/10.4018/ijssci.2018010102
Publication status	Published - Jan 2018

Bibliographical note

Acknowledgement
The authors would like to thank De Montfort University for the support given to enable this research and acknowledge that this work has been partly supported by the Spanish Ministry of Economy and Competitiveness through project TIN2014-59641-C2-1-P

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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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N2 - 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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SOM-Based Class Discovery for Emotion Detection Based on DEAP Dataset

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Bibliographical note

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