On using EEG signals for emotion modeling and biometry

Miguel Arevalillo-Herráez, Guillermo Chicote-Huete, Francesc J. Ferri, Aladdin Ayesh, Jesús G. Boticario, Stamos Katsigiannis, Naeem Ramzan, Pablo Arnau González

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A number of previous works have adopted a subject independent approach for recognizing emotions from Electroencephalography (EEG) signals, and attempted to build a global model by treating data from different subjects as if they belong to the same individual. In this paper we visually explore the data provided in four different standard datasets when using Power Spectral Density features, and show that the subject-dependent component in the EEG signal is far stronger than the emotion-related component. In addition, the session-dependency that is also found discourages the application of this type of features from EEG signals in a biometric context.
Original languageEnglish
Title of host publicationESM '2019 Conference Proceedings
EditorsPilar Fuster-Parra, Oscar Valero Sierra
PublisherEuropean Multidisciplinary Society for Modelling and Simulation Technology
Number of pages5
ISBN (Electronic)9789492859099
Publication statusPublished - 15 Oct 2019
Event33rd European Simulation and Modelling Conference - Universitat de les Illes Balears, Palma de Mallorca, Spain
Duration: 28 Oct 201930 Oct 2019


Conference33rd European Simulation and Modelling Conference
Abbreviated titleESM'2019
CityPalma de Mallorca
Internet address


  • EEG
  • Affect
  • Biometrics
  • Emotion modeling


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