Artificial Neural Networks to Assess Emotional States from Brain-Computer Interface

ESTIMATION OF HUMAN EMOTIONS PLAYS AN IMPORTANT ROLE IN THE DEVELOPMENT OF MODERN BRAIN-COMPUTER INTERFACE DEVICES LIKE THE EMOTIV EPOC+ HEADSET. IN THIS PAPER, WE PRESENT AN EXPERIMENT TO ASSESS THE CLASSIFICATION ACCURACY OF THE EMOTIONAL STATES PROVIDED BY THE HEADSET’S APPLICATION PROGRAMMING IN...

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Detalles Bibliográficos
Autores: Sánchez Reolid, Roberto, Vivente Querol, Miguel Angel, García Jiménez, Arturo Simón, Fernández Aguilar, María de la Luz, López Bonal, María Teresa, Fernández Caballero, Antonio, González López, Pascual
Tipo de recurso: artículo
Fecha de publicación:2018
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/40982
Acceso en línea:https://www.mdpi.com/2079-9292/7/12/384
https://hdl.handle.net/10578/40982
Access Level:acceso abierto
Palabra clave:Artificial neural network
Assessment
Brain-computer interface
Electroencephalography
Emotional state
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spelling Artificial Neural Networks to Assess Emotional States from Brain-Computer InterfaceSánchez Reolid, RobertoVivente Querol, Miguel AngelGarcía Jiménez, Arturo SimónFernández Aguilar, María de la LuzLópez Bonal, María TeresaFernández Caballero, AntonioGonzález López, PascualArtificial neural networkAssessmentBrain-computer interfaceElectroencephalographyEmotional stateESTIMATION OF HUMAN EMOTIONS PLAYS AN IMPORTANT ROLE IN THE DEVELOPMENT OF MODERN BRAIN-COMPUTER INTERFACE DEVICES LIKE THE EMOTIV EPOC+ HEADSET. IN THIS PAPER, WE PRESENT AN EXPERIMENT TO ASSESS THE CLASSIFICATION ACCURACY OF THE EMOTIONAL STATES PROVIDED BY THE HEADSET’S APPLICATION PROGRAMMING INTERFACE (API). IN THIS EXPERIMENT, SEVERAL SETS OF IMAGES SELECTED FROM THE INTERNATIONAL AFFECTIVE PICTURE SYSTEM (IAPS) DATASET ARE SHOWN TO SIXTEEN PARTICIPANTS WEARING THE HEADSET. FIRSTLY, THE PARTICIPANTS’ RESPONSES IN FORM OF A SELF-ASSESSMENT MANIKIN QUESTIONNAIRE TO THE EMOTIONS ELICITED ARE COMPARED WITH THE VALIDATED IAPS PREDEFINED VALENCE, AROUSAL AND DOMINANCE VALUES. AFTER STATISTICALLY DEMONSTRATING THAT THE RESPONSES ARE HIGHLY CORRELATED WITH THE IAPS VALUES, SEVERAL ARTIFICIAL NEURAL NETWORKS (ANNS) BASED ON THE MULTILAYER PERCEPTRON ARCHITECTURE ARE TESTED TO CALCULATE THE CLASSIFICATION ACCURACY OF THE EMOTIV EPOC+ API EMOTIONAL OUTCOMES. THE BEST RESULT IS OBTAINED FOR AN ANN CONFIGURATION WITH THREE HIDDEN LAYERS, AND 30, 8 AND 3 NEURONS FOR LAYERS 1, 2 AND 3, RESPECTIVELY. THIS CONFIGURATION OFFERS 85% CLASSIFICATION ACCURACY, WHICH MEANS THAT THE EMOTIONAL ESTIMATION PROVIDED BY THE HEADSET CAN BE USED WITH HIGH CONFIDENCE IN REAL-TIME APPLICATIONS THAT ARE BASED ON USERS’ EMOTIONAL STATES. THUS THE EMOTIONAL STATES GIVEN BY THE HEADSET’S API MAY BE USED WITH NO FURTHER PROCESSING OF THE ELECTROENCEPHALOGRAM SIGNALS ACQUIRED FROM THE SCALP, WHICH WOULD ADD A LEVEL OF DIFFICULTY.MDPI202520252018info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://www.mdpi.com/2079-9292/7/12/384https://hdl.handle.net/10578/40982reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésinfo:eu-repo/semantics/openAccessoai:ruidera.uclm.es:10578/409822026-05-27T07:36:41Z
dc.title.none.fl_str_mv Artificial Neural Networks to Assess Emotional States from Brain-Computer Interface
title Artificial Neural Networks to Assess Emotional States from Brain-Computer Interface
spellingShingle Artificial Neural Networks to Assess Emotional States from Brain-Computer Interface
Sánchez Reolid, Roberto
Artificial neural network
Assessment
Brain-computer interface
Electroencephalography
Emotional state
title_short Artificial Neural Networks to Assess Emotional States from Brain-Computer Interface
title_full Artificial Neural Networks to Assess Emotional States from Brain-Computer Interface
title_fullStr Artificial Neural Networks to Assess Emotional States from Brain-Computer Interface
title_full_unstemmed Artificial Neural Networks to Assess Emotional States from Brain-Computer Interface
title_sort Artificial Neural Networks to Assess Emotional States from Brain-Computer Interface
dc.creator.none.fl_str_mv Sánchez Reolid, Roberto
Vivente Querol, Miguel Angel
García Jiménez, Arturo Simón
Fernández Aguilar, María de la Luz
López Bonal, María Teresa
Fernández Caballero, Antonio
González López, Pascual
author Sánchez Reolid, Roberto
author_facet Sánchez Reolid, Roberto
Vivente Querol, Miguel Angel
García Jiménez, Arturo Simón
Fernández Aguilar, María de la Luz
López Bonal, María Teresa
Fernández Caballero, Antonio
González López, Pascual
author_role author
author2 Vivente Querol, Miguel Angel
García Jiménez, Arturo Simón
Fernández Aguilar, María de la Luz
López Bonal, María Teresa
Fernández Caballero, Antonio
González López, Pascual
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Artificial neural network
Assessment
Brain-computer interface
Electroencephalography
Emotional state
topic Artificial neural network
Assessment
Brain-computer interface
Electroencephalography
Emotional state
description ESTIMATION OF HUMAN EMOTIONS PLAYS AN IMPORTANT ROLE IN THE DEVELOPMENT OF MODERN BRAIN-COMPUTER INTERFACE DEVICES LIKE THE EMOTIV EPOC+ HEADSET. IN THIS PAPER, WE PRESENT AN EXPERIMENT TO ASSESS THE CLASSIFICATION ACCURACY OF THE EMOTIONAL STATES PROVIDED BY THE HEADSET’S APPLICATION PROGRAMMING INTERFACE (API). IN THIS EXPERIMENT, SEVERAL SETS OF IMAGES SELECTED FROM THE INTERNATIONAL AFFECTIVE PICTURE SYSTEM (IAPS) DATASET ARE SHOWN TO SIXTEEN PARTICIPANTS WEARING THE HEADSET. FIRSTLY, THE PARTICIPANTS’ RESPONSES IN FORM OF A SELF-ASSESSMENT MANIKIN QUESTIONNAIRE TO THE EMOTIONS ELICITED ARE COMPARED WITH THE VALIDATED IAPS PREDEFINED VALENCE, AROUSAL AND DOMINANCE VALUES. AFTER STATISTICALLY DEMONSTRATING THAT THE RESPONSES ARE HIGHLY CORRELATED WITH THE IAPS VALUES, SEVERAL ARTIFICIAL NEURAL NETWORKS (ANNS) BASED ON THE MULTILAYER PERCEPTRON ARCHITECTURE ARE TESTED TO CALCULATE THE CLASSIFICATION ACCURACY OF THE EMOTIV EPOC+ API EMOTIONAL OUTCOMES. THE BEST RESULT IS OBTAINED FOR AN ANN CONFIGURATION WITH THREE HIDDEN LAYERS, AND 30, 8 AND 3 NEURONS FOR LAYERS 1, 2 AND 3, RESPECTIVELY. THIS CONFIGURATION OFFERS 85% CLASSIFICATION ACCURACY, WHICH MEANS THAT THE EMOTIONAL ESTIMATION PROVIDED BY THE HEADSET CAN BE USED WITH HIGH CONFIDENCE IN REAL-TIME APPLICATIONS THAT ARE BASED ON USERS’ EMOTIONAL STATES. THUS THE EMOTIONAL STATES GIVEN BY THE HEADSET’S API MAY BE USED WITH NO FURTHER PROCESSING OF THE ELECTROENCEPHALOGRAM SIGNALS ACQUIRED FROM THE SCALP, WHICH WOULD ADD A LEVEL OF DIFFICULTY.
publishDate 2018
dc.date.none.fl_str_mv 2018
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://www.mdpi.com/2079-9292/7/12/384
https://hdl.handle.net/10578/40982
url https://www.mdpi.com/2079-9292/7/12/384
https://hdl.handle.net/10578/40982
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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