Detecting Emotion from EEG Signals Using the Emotive Epoc Device

  • Rafael Ramirez
  • Zacharias Vamvakousis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7670)

Abstract

The study of emotions in human-computer interaction has increased in recent years in an attempt to address new user needs. At the same time, it is possible to record brain activity in real-time and discover patterns to relate it to emotional states. This paper describes a machine learning approach to detect emotion from brain activity, recorded as electroencephalograph (EEG) with the Emotic Epoc device, during auditory stimulation. First, we extract features from the EEG signals in order to characterize states of mind in the arousal-valence 2D emotion model. Using these features we apply machine learning techniques to classify EEG signals into high/low arousal and positive/negative valence emotional states. The obtained classifiers may be used to categorize emotions such as happiness, anger, sadness, and calm based on EEG data.

Keywords

Coherence Stim Boronat 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Rafael Ramirez
    • 1
  • Zacharias Vamvakousis
    • 1
  1. 1.Department of Information and Communication TechnologiesUniversitat Pompeu FabraBarcelonaSpain

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