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Evaluating classifiers for Emotion Recognition using EEG
Blekinge Tekniska Högskola.
Blekinge Tekniska Högskola.
Blekinge Tekniska Högskola.ORCID iD: 0000-0002-8591-1035
Blekinge Tekniska Högskola.
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2013 (English)In: Foundations of Augmented Cognition: 7th International Conference, AC 2013, Held as Part of HCI International 2013, Las Vegas, NV, USA, July 21-26, 2013. Proceedings / [ed] Dylan D. Schmorrow & Cali M. Fidopiastis, Springer, 2013, Vol. Part IV, 492-501 p.Conference paper, Published paper (Refereed)
Abstract [en]

There are several ways of recording psychophysiology data from humans, for example Galvanic Skin Response (GSR), Electromyography (EMG), Electrocardiogram (ECG) and Electroencephalography (EEG). In this paper we focus on emotion detection using EEG. Various machine learning techniques can be used on the recorded EEG data to classify emotional states. K-Nearest Neighbor (KNN), Bayesian Network (BN), Artificial Neural Network (ANN) and Support Vector Machine (SVM) are some machine learning techniques that previously have been used to classify EEG data in various experiments. Five different machine learning techniques were evaluated in this paper, classifying EEG data associated with specific affective/emotional states. The emotions were elicited in the subjects using pictures from the International Affective Picture System (IAPS) database. The raw EEG data were processed to remove artifacts and a number of features were selected as input to the classifiers. The results showed that it is difficult to train a classifier to be accurate over large datasets (15 subjects) but KNN and SVM with the proposed features were reasonably accurate over smaller datasets (5 subjects) identifying the emotional states with an accuracy up to 77.78%.

Place, publisher, year, edition, pages
Springer, 2013. Vol. Part IV, 492-501 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 8027
National Category
Computer Science
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-42378DOI: 10.1007/978-3-642-39454-6_53ISBN: 978-3-642-39453-9 (print)ISBN: 978-3-642-39454-6 (print)OAI: oai:DiVA.org:lnu-42378DiVA: diva2:805282
Conference
HCI International
Available from: 2015-04-15 Created: 2015-04-15 Last updated: 2015-06-10Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf