Class discovery from semi-structured EEG data for affective computing and personalisation

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dc.cclicenceCC-BY-NCen
dc.contributor.authorAyesh, Aladdin, 1972-en
dc.contributor.authorArevalillo-Herraez, M.en
dc.contributor.authorAmau-Gonzalez, P.en
dc.date.acceptance2017-05-02en
dc.date.accessioned2018-03-21T12:21:57Z
dc.date.available2018-03-21T12:21:57Z
dc.date.issued2017-07
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractMany 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.en
dc.funderPartly supported by the Spanish Ministry of Economy and Competitiveness through project TIN2014- 59641-C2-1-Pen
dc.identifier.citationAyesh, A., Arevalillo-Herráez, M. and Arnau-González, P. Class discovery from semi-structured EEG data for affective computing and personalisation. 2017 IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC), Oxford, July 2017, pp. 96-101.en
dc.identifier.doihttps://doi.org/10.1109/icci-cc.2017.8109736
dc.identifier.urihttp://hdl.handle.net/2086/15559
dc.language.isoenen
dc.peerreviewedYesen
dc.projectidTIN2014- 59641-C2-1-Pen
dc.publisherIEEEen
dc.researchgroupMobile Cognitive Systems Research Groupen
dc.researchinstituteCyber Technology Institute (CTI)en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectElectroencephalographyen
dc.subjectSelf-organizing feature mapsen
dc.subjectBrain modelingen
dc.subjectFeature extractionen
dc.subjectEmotion recognitionen
dc.titleClass discovery from semi-structured EEG data for affective computing and personalisationen
dc.typeConferenceen

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