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A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents

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A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents

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dc.contributor.author Colomer Granero, Adrián es_ES
dc.contributor.author Fuentes-Hurtado, Félix José es_ES
dc.contributor.author Naranjo Ornedo, Valeriana es_ES
dc.contributor.author Guixeres Provinciale, Jaime es_ES
dc.contributor.author Ausin-Azofra, Jose Manuel es_ES
dc.contributor.author Alcañiz Raya, Mariano Luis es_ES
dc.date.accessioned 2017-06-20T11:35:07Z
dc.date.available 2017-06-20T11:35:07Z
dc.date.issued 2016-07-15
dc.identifier.issn 1662-5188
dc.identifier.uri http://hdl.handle.net/10251/83259
dc.description This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission es_ES
dc.description.abstract This work focuses on finding the most discriminatory or representative features that allow to classify commercials according to negative, neutral and positive effectiveness based on the Ace Score index. For this purpose, an experiment involving forty-seven participants was carried out. In this experiment electroencephalography (EEG), electrocardiography (ECG), Galvanic Skin Response (GSR) and respiration data were acquired while subjects were watching a 30-min audiovisual content. This content was composed by a submarine documentary and nine commercials (one of themthe ad under evaluation). After the signal pre-processing, four sets of features were extracted from the physiological signals using different state-of-the-art metrics. These features computed in time and frequency domains are the inputs to several basic and advanced classifiers. An average of 89.76% of the instances was correctly classified according to the Ace Score index. The best results were obtained by a classifier consisting of a combination between AdaBoost and RandomForest with automatic selection of features. The selected features were those extracted from GSR and HRV signals. These results are promising in the audiovisual content evaluation field by means of physiological signal processing. es_ES
dc.description.sponsorship This work has been supported by the Heineken Endowed Chair in Neuromarketing at the Universitat Politecnica de Valencia in order to research and apply new technologies and neuroscience in communication, distribution and consumption fields. en_EN
dc.language Inglés es_ES
dc.publisher Frontiers Media es_ES
dc.relation Heineken Endowed Chair in Neuromarketing at the Universitat Politecnica de Valencia es_ES
dc.relation.ispartof Frontiers in Computational Neuroscience es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Audiovisual Content Evaluation es_ES
dc.subject Effectiveness es_ES
dc.subject Physiological Signal es_ES
dc.subject Electroencephalography (EEG) es_ES
dc.subject Electrocardiography 18 (ECG) es_ES
dc.subject Galvanic Skin Response (GSR) es_ES
dc.subject Respiration es_ES
dc.subject Feature extraction es_ES
dc.subject Advanced Classifiers es_ES
dc.subject.classification TEORIA DE LA SEÑAL Y COMUNICACIONES es_ES
dc.subject.classification EXPRESION GRAFICA EN LA INGENIERIA es_ES
dc.title A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3389/fncom.2016.00074
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano - Institut Interuniversitari d'Investigació en Bioenginyeria i Tecnologia Orientada a l'Ésser Humà es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería Agronómica y del Medio Natural - Escola Tècnica Superior d'Enginyeria Agronòmica i del Medi Natural es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Telecomunicación - Escola Tècnica Superior d'Enginyers de Telecomunicació es_ES
dc.description.bibliographicCitation Colomer Granero, A.; Fuentes-Hurtado, FJ.; Naranjo Ornedo, V.; Guixeres Provinciale, J.; Ausin-Azofra, JM.; Alcañiz Raya, ML. (2016). A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents. Frontiers in Computational Neuroscience. 10(74):1-16. doi:10.3389/fncom.2016.00074 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.3389/fncom.2016.00074 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 16 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 10 es_ES
dc.description.issue 74 es_ES
dc.relation.senia 315957 es_ES
dc.identifier.pmcid PMC4945646 en_EN


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