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.
|Number of pages||12|
|Journal||International Journal of Software Science and Computational Intelligence|
|Publication status||Published - Jan 2018|
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