Emotion Recognition Using the Emotiv EPOC Device

Emotion plays an important role in the interaction between humans as emotion is fundamental to human experience, influencing cognition, perception, learning communication, and even rational decision-making. Therefore, studying emotion is indispensable. This paper aims at finding the relationships be...

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Published inNeural Information Processing pp. 394 - 399
Main Authors Pham, Trung Duy, Tran, Dat
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2012
SeriesLecture Notes in Computer Science
Subjects
AdaBoost.M1
EEG
Emotion recognition
Emotiv EPOC headset
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Abstract Emotion plays an important role in the interaction between humans as emotion is fundamental to human experience, influencing cognition, perception, learning communication, and even rational decision-making. Therefore, studying emotion is indispensable. This paper aims at finding the relationships between EEG signals and human emotions based on emotion recognition experiments that are conducted using the commercial Emotiv EPOC headset to record EEG signals while participants are watching emotional movies. Alpha, beta, delta and theta bands filtered from the recorded EEG signals are used to train and evaluate classifiers with different learning techniques including Support Vector Machine, k-Nearest Neighbour, Naïve Bayes and AdaBoost.M1. Our experimental results show that we can use the Emotiv headset for emotion recognition and that the AdaBoost.M1 technique and the theta band provide the highest recognition rates.
AbstractList Emotion plays an important role in the interaction between humans as emotion is fundamental to human experience, influencing cognition, perception, learning communication, and even rational decision-making. Therefore, studying emotion is indispensable. This paper aims at finding the relationships between EEG signals and human emotions based on emotion recognition experiments that are conducted using the commercial Emotiv EPOC headset to record EEG signals while participants are watching emotional movies. Alpha, beta, delta and theta bands filtered from the recorded EEG signals are used to train and evaluate classifiers with different learning techniques including Support Vector Machine, k-Nearest Neighbour, Naïve Bayes and AdaBoost.M1. Our experimental results show that we can use the Emotiv headset for emotion recognition and that the AdaBoost.M1 technique and the theta band provide the highest recognition rates.
Author Pham, Trung Duy
Tran, Dat
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  surname: Tran
  fullname: Tran, Dat
  email: dat.tran@canberra.edu.au
  organization: Faculty of Information Sciences and Engineering, University of Canberra, Australia
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Copyright Springer-Verlag Berlin Heidelberg 2012
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Leung, Chi Sing
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Snippet Emotion plays an important role in the interaction between humans as emotion is fundamental to human experience, influencing cognition, perception, learning...
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SubjectTerms AdaBoost.M1
EEG
Emotion recognition
Emotiv EPOC headset
Title Emotion Recognition Using the Emotiv EPOC Device
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