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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

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
Pages229-233
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
https://www.eurosis.org/conf/esm/2019/index.html

Conference

Conference33rd European Simulation and Modelling Conference
Abbreviated titleESM'2019
CountrySpain
CityPalma de Mallorca
Period28/10/1930/10/19
Internet address

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Electroencephalography
Power spectral density
Biometrics

Keywords

  • EEG
  • Affect
  • Biometrics
  • Emotion modeling

Cite this

Arevalillo-Herráez, M., Chicote-Huete, G., Ferri, F. J., Ayesh, A., Boticario, J. G., Katsigiannis, S., ... Arnau González, P. (2019). On using EEG signals for emotion modeling and biometry. In P. Fuster-Parra, & O. V. Sierra (Eds.), ESM '2019 Conference Proceedings (pp. 229-233). European Multidisciplinary Society for Modelling and Simulation Technology.
Arevalillo-Herráez, Miguel ; Chicote-Huete, Guillermo ; Ferri, Francesc J. ; Ayesh, Aladdin ; Boticario, Jesús G. ; Katsigiannis, Stamos ; Ramzan, Naeem ; Arnau González, Pablo. / On using EEG signals for emotion modeling and biometry. ESM '2019 Conference Proceedings. editor / Pilar Fuster-Parra ; Oscar Valero Sierra. European Multidisciplinary Society for Modelling and Simulation Technology, 2019. pp. 229-233
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Arevalillo-Herráez, M, Chicote-Huete, G, Ferri, FJ, Ayesh, A, Boticario, JG, Katsigiannis, S, Ramzan, N & Arnau González, P 2019, On using EEG signals for emotion modeling and biometry. in P Fuster-Parra & OV Sierra (eds), ESM '2019 Conference Proceedings. European Multidisciplinary Society for Modelling and Simulation Technology, pp. 229-233, 33rd European Simulation and Modelling Conference, Palma de Mallorca, Spain, 28/10/19.

On using EEG signals for emotion modeling and biometry. / Arevalillo-Herráez, Miguel; Chicote-Huete, Guillermo; Ferri, Francesc J.; Ayesh, Aladdin; Boticario, Jesús G.; Katsigiannis, Stamos; Ramzan, Naeem; Arnau González, Pablo.

ESM '2019 Conference Proceedings. ed. / Pilar Fuster-Parra; Oscar Valero Sierra. European Multidisciplinary Society for Modelling and Simulation Technology, 2019. p. 229-233.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Boticario, Jesús G.

AU - Katsigiannis, Stamos

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AB - 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.

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Arevalillo-Herráez M, Chicote-Huete G, Ferri FJ, Ayesh A, Boticario JG, Katsigiannis S et al. On using EEG signals for emotion modeling and biometry. In Fuster-Parra P, Sierra OV, editors, ESM '2019 Conference Proceedings. European Multidisciplinary Society for Modelling and Simulation Technology. 2019. p. 229-233