Abstract
Many approaches to recognising emotions from metrical data such as EEG signals rely on identifying a very small number of classes and to train a classifier. The interpretation of these classes varies from a single emotion such as stress [24] to features of emotional model such as valence-arousal [4]. There are two major issues here. First classification approach limits the analysis of the data within the selected classes and is also highly dependent on training data/cycles, all of which limits generalisation. Second issue is that it does not explore the inter-relationships between the data collected missing out on any correlations that could tell us interesting facts beyond emotional recognition. This second issue would be of particular interest to psychologists and medical professions. In this paper, we investigate the use of Self-Organizing Maps (SOM) in identifying clusters from EEG signals that could then be translated into classes. We start by training varying sizes of SOM with the EEG data provided in a public dataset (DEAP). The produced graphs showing Neighbour Distance, Sample Hits, Weight Position are analysed holistically to identify patterns in the structure. Following that, we have considered the ground-truth label provided in DEAP, in order to identify correlations between the label and the clustering produced by the SOM. The results show the potential of SOM for class discovery in this particular context. We conclude with a discussion on the implications of this work and the difficulties in evaluating the outcome.
Original language | English |
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Title of host publication | Proceedings of 2017 IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017 |
Editors | Yingxu Wang, Freddie Hamdy, Newton Howard, Lotfi A. Zadeh, Amir Hussain, Bernard Widrow |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 96-101 |
Number of pages | 6 |
ISBN (Electronic) | 9781538607701 |
DOIs | |
Publication status | Published - 16 Nov 2017 |
Externally published | Yes |
Event | 16th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017 - Oxford, United Kingdom Duration: 26 Jul 2017 → 28 Jul 2017 |
Publication series
Name | Proceedings of 2017 IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017 |
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Conference
Conference | 16th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017 |
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Country/Territory | United Kingdom |
City | Oxford |
Period | 26/07/17 → 28/07/17 |
Bibliographical note
Funding Information:ACKNOWLEDGMENT 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
Publisher Copyright:
© 2017 IEEE.