On using EEG signals for emotion modeling and biometrics

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

On using EEG signals for emotion modeling and biometrics

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 proceeding › Published conference contribution

2 Citations (Scopus)

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 language	English
Title of host publication	33rd Annual European Simulation and Modelling Conference 2019, ESM 2019
Editors	Pilar Fuster-Parra, Oscar Valero Sierra
Publisher	EUROSIS
Pages	229-233
Number of pages	5
ISBN (Electronic)	9789492859099
Publication status	Published - 2019
Externally published	Yes
Event	33rd Annual European Simulation and Modelling Conference, ESM 2019 - Plama de Mallorca, Spain Duration: 28 Oct 2019 → 30 Oct 2019

Conference

Conference	33rd Annual European Simulation and Modelling Conference, ESM 2019
Country/Territory	Spain
City	Plama de Mallorca
Period	28/10/19 → 30/10/19

Bibliographical note

Funding Information:
This research has been partly supported by CECOTEC INNOVACIONES, S.L. and the Spanish Ministry of Economy and Competitiveness through projects TIN2014-59641-C2-1-P and PGC2018-096463-B-I00.

Publisher Copyright:
Copyright © 2019 EUROSIS-ETI.

Keywords

Affect
Biometrics
EEG
Emotion modeling

Cite this

On using EEG signals for emotion modeling and biometrics. / Arevalillo-Herráez, Miguel; Chicote-Huete, Guillermo; Ferri, Francesc J. et al.
33rd Annual European Simulation and Modelling Conference 2019, ESM 2019. ed. / Pilar Fuster-Parra; Oscar Valero Sierra. EUROSIS, 2019. p. 229-233.

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

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 biometrics. in P Fuster-Parra & OV Sierra (eds), 33rd Annual European Simulation and Modelling Conference 2019, ESM 2019. EUROSIS, pp. 229-233, 33rd Annual European Simulation and Modelling Conference, ESM 2019, Plama de Mallorca, Spain, 28/10/19.

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title = "On using EEG signals for emotion modeling and biometrics",

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.",

keywords = "Affect, Biometrics, EEG, Emotion modeling",

author = "Miguel Arevalillo-Herr{\'a}ez and Guillermo Chicote-Huete and Ferri, {Francesc J.} and Aladdin Ayesh and Boticario, {Jes{\'u}s G.} and Stamos Katsigiannis and Naeem Ramzan and Pablo Arnau-Gonz{\'a}lez",

note = "Funding Information: This research has been partly supported by CECOTEC INNOVACIONES, S.L. and the Spanish Ministry of Economy and Competitiveness through projects TIN2014-59641-C2-1-P and PGC2018-096463-B-I00. Publisher Copyright: Copyright {\textcopyright} 2019 EUROSIS-ETI.; 33rd Annual European Simulation and Modelling Conference, ESM 2019 ; Conference date: 28-10-2019 Through 30-10-2019",

year = "2019",

language = "English",

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AU - Chicote-Huete, Guillermo

AU - Ferri, Francesc J.

AU - Ayesh, Aladdin

AU - Boticario, Jesús G.

AU - Katsigiannis, Stamos

AU - Ramzan, Naeem

AU - Arnau-González, Pablo

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PY - 2019

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N2 - 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.

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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KW - Biometrics

KW - EEG

KW - Emotion modeling

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M3 - Published conference contribution

AN - SCOPUS:85076203732

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BT - 33rd Annual European Simulation and Modelling Conference 2019, ESM 2019

A2 - Fuster-Parra, Pilar

A2 - Sierra, Oscar Valero

PB - EUROSIS

T2 - 33rd Annual European Simulation and Modelling Conference, ESM 2019

Y2 - 28 October 2019 through 30 October 2019

ER -

On using EEG signals for emotion modeling and biometrics

Abstract

Conference

Bibliographical note

Keywords

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