Reconocimiento de emociones en humanos mediante procesamiento de señales EEG y estimulación auditiva

dc.contributor.advisorNiño Vásquez, Luis Fernando
dc.contributor.authorOrtega Loaiza, Christian Ortega
dc.contributor.researchgroupLABORATORIO DE INVESTIGACIÓN EN SISTEMAS INTELIGENTES - LISIspa
dc.date.accessioned2021-07-22T14:11:19Z
dc.date.available2021-07-22T14:11:19Z
dc.date.issued2021
dc.descriptionilustraciones, fotografías, tablasspa
dc.description.abstractEste trabajo aborda una problemática que no es ajena a la academia, pero que aún presenta resultados embrionarios. En particular, emplea estímulos auditivos con el objeto de implementar un algoritmo computacional que realice el reconocimiento de un grupo definido de emociones maximizando la precisión y reduciendo la cantidad de electrodos necesarios para dicha tarea. Para ello se definió un grupo de 6 emociones objetivo estimuladas mediante 30 audios, los cuales fueron presentados a un grupo de 14 personas voluntarias, de entre 18 y 35 años, sobre las cuales se realizó la lectura de las señales EEG. La metodología conllevó 3 fases, son sus respectivas etapas, y permitió construir un algoritmo basado tanto en características convencionales como en la Transformada Wavelet, la Dimensión Fractal y un modelo de Análisis Discriminante Cuadrático, el cual fue valorado bajo métricas de precisión, exactitud, exhaustividad y puntaje F1. Los resultados fueron comparados con aquellos reportados en otros trabajos similares disponibles en la literatura. (Texto tomado de la fuente)spa
dc.description.abstractThis work addresses a problem that is not beyond to academia, but which still presents embryonic results. In particular, it uses auditory stimuli in order to implement a computational algorithm that performs the recognition of a defined group of emotions maximizing accuracy and reducing the number of electrodes needed for this task. To this end, a group of 6 target emotions stimulated by 30 audio excerpts were defined and presented to a group of 14 volunteers, aged between 18 and 35, on whom the EEG signals were read. The methodology involved 3 phases, with their respective stages, and allowed the construction of an algorithm based on conventional features as well as on Wavelet Transform, Fractal Dimension and a Quadratic Discriminant Analysis model, which was evaluated under metrics of precision, accuracy, recall and F1 score. The results were compared with those reported in other similar works available in the literature. (Text taken from source)eng
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ingeniería - Ingeniería de Sistemas y Computaciónspa
dc.description.researchareaSistemas inteligentesspa
dc.format.extent122 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/79832
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.departmentDepartamento de Ingeniería de Sistemas e Industrialspa
dc.publisher.facultyFacultad de Ingenieríaspa
dc.publisher.placeBogotá, Colombiaspa
dc.publisher.programBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computaciónspa
dc.relation.referencesBadcock, Nicholas A. ; Mousikou, Petroula ; Mahajan, Yatin ; de Lissa, Peter ; Thie, Johnson ; McArthur, Genevieve: Validation of the Emotiv EPOC(®) EEG gaming system for measuring research quality auditory ERPs. En: PeerJ 1 (2013), Nr.1, p. e38spa
dc.relation.referencesBos, Danny O.: EEG-based emotion recognition. En: The Influence of Visual and Auditory Stimuli (2006), p. 1–17spa
dc.relation.referencesBradley, Margaret M. ; Lang, Peter J.: Measuring emotion: The self-assessment ma- nikin and the semantic differential. En: Journal of Behavior Therapy and Experimental Psychiatry 25 (1994), Nr. 1, p. 49–59spa
dc.relation.referencesBradley, Margaret M. ; Lang, Peter J.: The International Affective Digitized Sounds (2nd Edition; IADS-2): Affective ratings of sounds and instruction manual. Technical report B-3. En: Technical report B-3. (2007)spa
dc.relation.referencesCabredo, Rafael ; Legaspi, Roberto ; Inventado, Paul S. ; Numao, Masayuki: Dis- covering emotion-inducing music features using EEG signals. En: Journal of Advanced Computational Intelligence and Intelligent Informatics 17 (2013), p. 362–370. – ISSN 13430130spa
dc.relation.referencesCandra, Henry ; Yuwono, Mitchell ; Handojoseno, Ardi ; Chai, Rifai ; Su, Steven ; Nguyen, Hung T.: Recognizing emotions from EEG subbands using wavelet analy- sis. En: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS Vol. 2015-Novem, Institute of Electrical and Electronics Engineers Inc., 2015, p. 6030–6033spa
dc.relation.referencesCernea, Daniel ; Kerren, Andreas ; Ebert, Achim: Detecting insight and emotion in visualization applications with a commercial EEG headset. En: SIGRAD 2011 Confe- rence on Evaluations of Graphics and Visualization-Efficiency, Usefulness, Accessibility, Usability,(Stockholm, Sweden), 2011, p. 53–60spa
dc.relation.referencesChanel, Guillaume ; Kronegg, Julien ; Grandjean, Didier ; Pun, Thierry: Emotion assessment: Arousal evaluation using EEG’s and peripheral physiological signals. En: Multimedia content representation, classification and security. Springer, 2006, p. 530– 537spa
dc.relation.referencesChawla, Nitesh V. ; Hall, Lawrence O. ; Kegelmeyer, W. P. ; Bowyer, Kevin W.: SMOTE: Synthetic Minority Over-sampling Technique. En: Journal of Artificial Inte- lligence Research 16 (2002), Nr. 1, p. 321–357. – ISBN 013805326Xspa
dc.relation.referencesCui, Zhicheng ; Chen, Wenlin ; Chen, Yixin: Multi-Scale Convolutional Neural Net- works for Time Series Classification. En: ArXiv preprint arXiv:1603.06995v4 [cs.CV] (2016)spa
dc.relation.referencesDelorme, Arnaud ; Rousselet, Guillaume A. ; Macé, Marc J M. ; Fabre-Thorpe, Michèle: Interaction of top-down and bottom-up processing in the fast visual analysis of natural scenes. En: Cognitive Brain Research 19 (2004), Nr. 2, p. 103–113spa
dc.relation.referencesElgendi, Mohamed ; Rebsamen, Brice ; Cichocki, Andrzej ; Vialatte, Francois ; Dauwels, Justin: Real-time wireless sonification of brain signals. En: Advances in Cognitive Neurodynamics (III). Springer, 2013, p. 175–181spa
dc.relation.referencesEscobar, Maria ; Novoa, Edgar: Análisis de formatos de consentimiento informado en Colombia. Problemas ético-legales y dificultades en el lenguaje. En: Revista Latino- americana de Bioética 16(1) (2016), p. 14–37spa
dc.relation.referencesGuennec, Arthur L. ; Malinowski, Simon ; Tavenard, Romain: Data Augmentation for Time Series Classification using Convolutional Neural Networks. En: ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data, 2016spa
dc.relation.referencesGuyon, Isabelle ; Wenston, Jason ; Barnhill, Stephen ; Vapnik, Vladimir: Ge- ne Selection for Cancer Classification using Support Vector Machines. En: Machine Learning 46 (2002), Nr. 1-3, p. 389–422. – ISSN 1573–0565spa
dc.relation.referencesHadjidimitriou, Stelios K. ; Hadjileontiadis, Leontios J.: Toward an EEG-based recognition of music liking using time-frequency analysis. En: IEEE Transactions on Biomedical Engineering 59 (2012), Nr. 12, p. 3498–3510spa
dc.relation.referencesHantke, Simone ; Weninger, Felix ; Han, Wenjing ; Zhang, Zixing ; Narayanan, Shrikanth: Automatic recognition of emotion evoked by general sound events. En: Icassp2012 (2012), Nr. Section 2, p. 341–344. ISBN 9781467300469spa
dc.relation.referencesHiguchi, T: Approach to an irregular time series on the basis of the fractal theory. En: Physica D: Nonlinear Phenomena 31 (1988), Nr. 2, p. 277–283spa
dc.relation.referencesJatupaiboon, N. ; Pan-ngum, S. ; Israsena, P.: Emotion classification using minimal EEG channels and frequency bands. En: The 2013 10th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2013, p. 21–24spa
dc.relation.referencesKesić, Srdjan ; Spasić, Sladjana Z.: Application of Higuchi’s fractal dimension from basic to clinical neurophysiology: A review. En: Computer Methods and Programs in Biomedicine (2016). – ISSN 18727565spa
dc.relation.referencesKlonowski, Wlodzimierz: Everything you wanted to ask about EEG but were afraid to get the right answer. En: Nonlinear Biomedical Physics 3 (2009), Nr. 1. – ISSN 1753–4631spa
dc.relation.referencesKoelsch, Stefan ; Fritz, Thomas ; Müller, Karsten ; Friederici, Angela D. [u. a.]: Investigating emotion with music: an fMRI study. En: Human brain mapping 27 (2006), Nr. 3, p. 239–250spa
dc.relation.referencesKoelstra, S. ; Muhl, C. ; Soleymani, M. ; Jong-Seok Lee ; Yazdani, A. ; Ebrahimi, T. ; Pun, T. ; Nijholt, A. ; Patras, I.: DEAP: A Database for Emotion Analysis ;Using Physiological Signals. En: IEEE Transactions on Affective Computing 3 (2012), jan, Nr. 1, p. 18–31. – ISSN 1949–3045spa
dc.relation.referencesKolodziej, Marcin ; Majkowski, Andrzej ; Rak, Remigiusz J.: A new method of spatial filters design for brain-computer interface based on steady state visually evoked potentials. En: 2015 IEEE 8th International Conference on Intelligent Data Acquisi- tion and Advanced Computing Systems: Technology and Applications (IDAACS) Vol. 2, IEEE, 2015. – ISBN 978–1–4673–8359–2, p. 697–700spa
dc.relation.referencesKvaale, S. P.: Emotion Recognition in EEG: A neuroevolutionary approach., Norwe- gian University of Science and Technology, Tesis de Grado, 2012spa
dc.relation.referencesLee, Gregory ; Gommers, Ralf ; Waselewski, Filip ; Wohlfahrt, Kai ; O’Leary, Aaron: PyWavelets: A Python package for wavelet analysis. Journal of Open Source Software. En: The Journal of Open Source 4 (2019), Nr. 36, p. 1237spa
dc.relation.referencesLi, Ma ; Chai, Quek ; Kaixiang, Teo ; Wahab, Abdul ; Abut, Hüseyin: EEG emotion recognition system. En: In-vehicle corpus and signal processing for driver behavior. Springer, 2009, p. 125–135spa
dc.relation.referencesLin, Yuan P. ; Wang, Chi H. ; Jung, Tzyy P. ; Wu, Tien L. ; Jeng, Shyh K. ; Duann, Jeng R. ; Chen, Jyh H.: EEG-based emotion recognition in music listening. En: IEEE Transactions on Biomedical Engineering 57 (2010), Nr. 7, p. 1798–1806spa
dc.relation.referencesLin, Yuan P. ; Wang, Chi H. ; Wu, Tien L. ; Jeng, Shyh K. ; Chen, Jyh H.: Mul- tilayer perceptron for EEG signal classification during listening to emotional music. En: IEEE Region 10 Annual International Conference, Proceedings/TENCON (2007). ISBN 1424412722spa
dc.relation.referencesLin, Yuan-Pin ; Wang, Chi-Hong ; Wu, Tien-Lin ; Jeng, Shyh-Kang ; Chen, Jyh- Horng: EEG-based emotion recognition in music listening: A comparison of schemes for multiclass support vector machine. En: Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on IEEE, 2009, p. 489–492spa
dc.relation.referencesLiu, Yisi ; Sourina, Olga: EEG databases for emotion recognition. En: Proceedings - 2013 International Conference on Cyberworlds, CW 2013, IEEE Computer Society, 2013, p. 302–309spa
dc.relation.referencesLiu, Yisi ; Sourina, Olga ; Nguyen, Minh K.: Real-time EEG-based emotion recog- nition and its applications. En: Transactions on computational science XII. Springer, 2011, p. 256–277spa
dc.relation.referencesMohammadi, Zeynab ; Frounchi, Javad ; Amiri, Mahmood: Wavelet-based emotion recognition system using EEG signal. En: Neural Computing and Applications 28 (2017), Aug, Nr. 8, p. 1985–1990. – ISSN 1433–3058spa
dc.relation.referencesMurugappan, M. ; Nagarajan, R. ; Yaacob, Sazali: Comparison of different wavelet features from EEG signals for classifying human emotions. En: 2009 IEEE Symposium on Industrial Electronics & Applications Vol. 2, IEEE, Oktober 2009. – ISBN 978–1– 4244–4681–0, p. 836–841spa
dc.relation.referencesMurugappan, M ; Rizon, M ; Nagarajan, Ramachandran ; Yaacob, S ; Zunaidi, I ; Hazry, Desa: Lifting scheme for human emotion recognition using EEG. En: Information Technology, 2008. ITSim 2008. International Symposium on Vol. 2 IEEE, 2008, p. 1–7spa
dc.relation.referencesMurugappan, Murugappan ; Ramachandran, Nagarajan ; Sazali, Yaacob: Classi- fication of human emotion from EEG using discrete wavelet transform. En: Journal of Biomedical Science and Engineering 3 (2010), p. 390–396spa
dc.relation.referencesOikonomou, Vangelis P. ; Liaros, Georgios ; Georgiadis, Kostantinos ; Chatzila- ri, Elisavet ; Adam, Katerina ; Nikolopoulos, Spiros ; Kompatsiaris, Ioannis: Com- parative evaluation of state-of-the-art algorithms for SSVEP-based BCIs. En: CoRR abs/1602.00904 (2016)spa
dc.relation.referencesOlejarczyk, Elżbieta: Application of fractal dimension method of functional MRI time-series to limbic dysregulation in anxiety study, IEEE, 2007. – ISBN 978–1–4244– 0787–3spa
dc.relation.referencesPlutchik, Robert: A psychoevolutionary theory of emotions. En: Social Science Information 21 (1982), Nr. 4-5, p. 529–553spa
dc.relation.referencesPongpanitanont, P ; Sittiprapaporn, W ; Charoensuk, W [u. a.]: Pattern re- cognition in brain FMRI for agnosia. En: Int J Appl Biomed Eng 3 (2010), p. 39–44spa
dc.relation.referencesRagot, Nicolas ; Bouzbouz, F. ; Khemmar, R. ; Kokosy, Anne-Marie ; Labbani- Igbida, Ouiddad ; Sajous, Patricia ; Niyonsaba, Emmanuel ; Reguer, D. ; Hu, Huosheng ; McDonald-Maier, Klaus ; Sirlantzis, Kostas ; Howells, Gareth ; Pepper, Matthew ; Sakel, M.: Enhancing the Autonomy of Disabled Persons: Assis- tive Technologies Directed by User Feedback. En: 2013 Fourth International Conference on Emerging Security Technologies, 2010, p. 71–74spa
dc.relation.referencesRanky, GN ; Adamovich, S: Analysis of a commercial EEG device for the control of a robot arm. En: Bioengineering Conference, Proceedings of the 2010 IEEE 36th Annual Northeast IEEE, 2010, p. 1–2spa
dc.relation.referencesRussell, James A.: A circumplex model of affect. En: Journal of personality and social psychology 39 (1980), Nr. 6, p. 1161spa
dc.relation.referencesSchalk, Gerwin ; McFarland, Dennis J. ; Hinterberger, Thilo ; Birbaumer, Niels ; Wolpaw, Jonathan R.: BCI2000: A general-purpose brain-computer interface (BCI) system. En: IEEE Transactions on Biomedical Engineering 51 (2004), Nr. 6, p. 1034–1043spa
dc.relation.referencesScherer, Klaus R.: What are emotions? And how can they be measured? En: Social Science Information 44 (2005), Nr. 4, p. 695–729spa
dc.relation.referencesSchuller, Böjrn ; Dorfner, Johannes ; Rigoll, Gerhard: Determination of nonpro- totypical valence and arousal in popular music: Features and performances. En: Eurasip Journal on Audio, Speech, and Music Processing 2010 (2010). – ISSN 16874714spa
dc.relation.referencesSmits, Fenne M. ; Porcaro, Camillo ; Cottone, Carlo ; Cancelli, Andrea ; Rossi- ni, Paolo M. ; Tecchio, Franca: Electroencephalographic Fractal Dimension inHealthy Ageing and Alzheimer’s Disease. En: PLoS ONE 11, Nr. 2spa
dc.relation.referencesSourina, Olga ; Liu, Yisi: A Fractal-based Algorithm of Emotion Recognition from EEG using Arousal-Valence Model. En: BIOSIGNALS, 2011, p. 209–214spa
dc.relation.referencesStevenson, Ryan A. ; James, Thomas W.: Affective auditory stimuli: Characteri- zation of the International Affective Digitized Sounds (IADS) by discrete emotional categories. En: Behavior Research Methods 40 (2008), Februar, Nr. 1, p. 315–321. – ISSN 1554–351Xspa
dc.relation.referencesVareka, Lukas ; Bruha, Petr ; Moucek, Roman: Event-related potential datasets based on a three-stimulus paradigm. En: GigaScience 3 (2014), Nr. 1, p. 35spa
dc.relation.referencesVokorokos, Liberios ; Madoš, Branislav ; Ádám, Norbert ; Baláž, Anton: Data Ac- quisition in Non-Invasive Brain-Computer Interface Using Emotiv Epoc Neuroheadset. En: Acta Electrotechnica et Informatica 12 (2012), Nr. 1, p. 5–8spa
dc.relation.referencesWen, Qingsong ; Sun, Liang ; Song, Xiaomin ; Gao, Jingkun ; Wang, Xue ; Xu, Huan: Time Series Data Augmentation for Deep Learning: A Survey. En: ArXiv pre- print abs/2002.12478 (2020)spa
dc.relation.referencesYohanes, Rendi E J. ; Ser, Wee ; Huang, Guang-bin: Discrete wavelet transform coefficients for emotion recognition from EEG signals. En: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 2012 (2012), p. 2251–4. – ISBN 9781457717871spa
dc.relation.referencesZhang, X L. ; Begleiter, H ; Porjesz, B ; Wang, W ; Litke, a: Event related potentials during object recognition tasks. En: Brain research bulletin 38 (1995), Nr. 6, p. 531–538. – ISBN 0361–9230 (Print)\r0361–9230 (Linking)spa
dc.rightsDerechos reservados al autor, 2021spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial-CompartirIgual 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/spa
dc.subject.ddc000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresspa
dc.subject.decsEmociones
dc.subject.decsEmotions
dc.subject.proposalAprendizaje Automáticospa
dc.subject.proposalDimensión Fractalspa
dc.subject.proposalEmocionesspa
dc.subject.proposalInterfaces Cerebro-Computadorspa
dc.subject.proposalQDAeng
dc.subject.proposalWavelet Analysiseng
dc.subject.proposalMachine Learningeng
dc.subject.proposalFractal Dimensioneng
dc.subject.proposalEmotionseng
dc.subject.proposalElectroencephalographyeng
dc.subject.proposalBrain-Computer Interfaceseng
dc.subject.proposalAnálisis de ondículasspa
dc.subject.proposalElectroencefalografíaspa
dc.subject.unescoInvestigación sobre el cerebro
dc.subject.unescoBrain research
dc.titleReconocimiento de emociones en humanos mediante procesamiento de señales EEG y estimulación auditivaspa
dc.title.translatedHuman emotion recognition using EEG signal processing and auditory stimulationeng
dc.title.translatedMenschliche Emotionserkennung mittels EEG-Signalverarbeitung und auditorischer Stimulationdeu
dc.title.translatedReconnaissance des émotions humaines par le traitement du signal EEG et la stimulation auditivefra
dc.title.translatedReconhecimento das emoções humanas usando o processamento de sinais EEG e estimulação auditivapor
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TMspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audienceGeneralspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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