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Affective recognition from EEG signals: an integrated data-mining approach
Department of Electronic and Systems Engineering, Universidad de la Costa, CUC, Barranquilla, Colombia.ORCID iD: 0000-0002-2755-0841
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-3495-2961
Department of Industrial Management, Agroindustry and Operations, Universidad de la Costa, CUC, Barranquilla, Colombia.ORCID iD: 0000-0001-6890-7547
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2019 (English)In: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, Vol. 10, no 10, p. 3955-3974Article in journal (Refereed) Published
Abstract [en]

Emotions play an important role in human communication, interaction, and decision making processes. Therefore, considerable efforts have been made towards the automatic identification of human emotions, in particular electroencephalogram (EEG) signals and Data Mining (DM) techniques have been then used to create models recognizing the affective states of users. However, most previous works have used clinical grade EEG systems with at least 32 electrodes. These systems are expensive and cumbersome, and therefore unsuitable for usage during normal daily activities. Smaller EEG headsets such as the Emotiv are now available and can be used during daily activities. This paper investigates the accuracy and applicability of previous affective recognition methods on data collected with an Emotiv headset while participants used a personal computer to fulfill several tasks. Several features were extracted from four channels only (AF3, AF4, F3 and F4 in accordance with the 10–20 system). Both Support Vector Machine and Naïve Bayes were used for emotion classification. Results demonstrate that such methods can be used to accurately detect emotions using a small EEG headset during a normal daily activity. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

Place, publisher, year, edition, pages
Heidelberg: Springer Berlin/Heidelberg, 2019. Vol. 10, no 10, p. 3955-3974
Keywords [en]
Affective recognition, Statistical features, Affective computing, Electroencephalogram (EEG), Data Mining (DM)
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-40797DOI: 10.1007/s12652-018-1065-zISI: 000487047400018Scopus ID: 2-s2.0-85054326423OAI: oai:DiVA.org:hh-40797DiVA, id: diva2:1366912
Note

Funders: REMIND Project from the European Union's Horizon 2020 research and innovation programme (734355), European Cooperation in Science and Technology (COST) (COST-STSM-TD1405- 33385) & National Council for Scientific and Technological Development (CNPq).

Available from: 2019-10-31 Created: 2019-10-31 Last updated: 2019-10-31

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Menezes, Maria Luiza RecenaPinheiro Sant'Anna, Anita

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Mendoza-Palechor, FabioMenezes, Maria Luiza RecenaPinheiro Sant'Anna, AnitaOrtiz-Barrios, MiguelSamara, Anas
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