Análisis espectral de parámetros fisiológicos para la detección de emociones
DOI:
https://doi.org/10.18046/syt.v10i20.1148Palabras clave:
Electro encefalograma (EEG), emociones, procesamiento de señales, Transformada Discreta Wavelet (DWT), interfaces cerebro-computador (BCI), redes neuronalesResumen
En el trabajo se aborda una revisión bibliográfica en el campo de la detección de emociones. Se muestra el gran impacto de las aplicaciones de interfaz cerebro-computador (BCI) y su funcionalidad. Se explica cómo trabajar con las señales neuronales y cómo extraer información de ellas utilizando la Transformada Discreta Wavelet (DWT). También se mencionan los métodos que se usan para la clasificación y caracterización. El objetivo del escrito es revisar el estado del arte para establecer las bases de un proyecto que busca analizar los parámetros del cerebro cuando un televidente observa un comercial publicitario.
Referencias
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Kandaswamy, A., Kumar, C. S., Ramanathan, R., Jayaraman, S., & Malmurugan, N. (2004). Neural classification of lung sound using wavelet coefficients. Computers on Biology and Medicine, 34(6):523-537.
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Learned, R. E., & Willsky, A. S. (1995). A Wavelet Packet Approach to transient signal classification. Applied Computer Harmonic Analysis, 2, 265-278. http://ssg.mit.edu/~willsky/publ_pdfs/129_pub_ACH.pdf
Mallat, S. G. (1989). A theory for multi-resolution signal decomposition: The Wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674-693.
Mukul, M. K., & Matsuno, F. (2009). EEG de-noising based on wavelet transforms and extraction of sub-band components related to movement imagination. En ICAAS-SICE, 2009, Fukuoka, Japón, pp. 1605-1610. Tokyo, Japón: The Society of Instrument and Control Engineers (SICE)
Murugappan, M., Nagarajan, R., & Yaacob, S. (2010). Classification of human emotion from EEG using discrete Wavelet transform. Journal of Biomedical Science and Engineering, 3(4), 390-396. doi: 10.4236/jbise.2010.34054
Murugappan, M., Rizon, M., Nagarajan, R., & Yaacob, S. (2009). An Investigation on visual and audiovisual stimulus based emotion recognition using EEG. International Journal of Medical Engineering and Informatics, 1(3) pp. 342-356. doi: 10.1504/IJMEI.2009.022645
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Pei, X. & Zheng, C (2008). Classification of left and right hand motor imagery tasks based on EEG frecuency component selection. En The 2nd International conference on bioinformatics and biomedical engineering,ICBBE 2008, pp. 1888-1891. Piscataway, NJ: IEEE. doi: 10.1109/ICBBE.2008.801
Picard, R. W. (2000). Affective Computing. Cambridge, MA: MIT Press.
Picard, R. W., Vyzas, E., & Healey, J. (2001.). Towards Machine Emotional Intelligence: Analysis of Affective Physiological State. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(10), 1175-1191.
Procházka, A., Kukal, J., & Vysata, O. (2008). Wavelet transform use for feature extraction and EEG signal segments classification. En 3rd International Symposium on Communications, Control and Signal Processing, 2008. ISCCSP 2008, St. Juliens, Malta, pp. 719-722. Piscataway, NJ: IEEE.
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Sherwood, J., & Derakhshani, R. (2009). On classifiability of wavelet features for EEG-basedbrain-computer interfaces. En International joint conference on neural networks, 2009. IJCNN 2009, pp. 2895-2902. Piscataway, NJ: IEEE. doi: 10.1109/IJCNN.2009.5178939
Sørensen, J. (2008). Measuring emotions in a consumer decision making context – Approaching or avoiding [working paper] Aalborg University, Dinamarca. Recuperado de http://www.business.aau.dk/wp/08-20.pdf
Subasia, A., & Ercelebi, E. (2005). Classification of EEG signals using neural network and logistic regression. Computer Methods and Programs in Biomedicine, 78(2), 87-89.
Vélez, P., & Saldarriaga, H. (2010). Clasificación básica de neuroseñales [Tesis de maestría]. Universidad Tecnológica de Pereira.
Windhorst, U. (1999). Modern Techniques in Neuroscience Research. Berlín, Alemania: Springer Verlag.
Wolpaw, J., Birbaumer, N., McFarland, D., Pfurtscheller, G., & Vaughan, T. (2002.). Brain computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767-791.
Young, C. (2002). Brain waves, picture sorts, and branding moments. Journal of Advertising Research, 42(4). 42-53
Yu, L. (2009). EEG De-Noising Based on Wavelet Transformation. En 3rd International Conference on Bioinformatics and Biomedical Engineering. Beijing, China, pp. 1-4. New York, NY: Curran. doi: 10.1109/ICBBE.2009.5162680
Zhang, X., Yin, L., & Wang, W. (2011). Wavelet Time-frecuency Analysus of Electro- encephalogram (EEG) Processing. International Journal of Advanced Computer Science and Applications (IJACSA), 1(5), 1-5.
AlMejrad, A.S. (2010). Human emotions detection using brain wave signals: A challenging. European Journal of Scientific Research, 44(4), 640-659.
Anderson, C. W., & Sijiercic, Z. (1997). Classification of EEG signals from Four Subjects during five mental tasks. En IEEE Proceedings of the conference on engineering application in neural networks, EANN´96. pp. 407-414. http://sce.uhcl.edu/boetticher/CSCI5931%20Computer%20Human%20Interaction/Classification%20of%20EEG%20signals%20from%20four%20subjects%20during%20five%20mental%20tasks.pdf
Arnold, M. B. (1960). Emotion and personality. New York, NY: Columbia University Press.
Ballesteros, D. (2004). Aplicación de la transformada Wavelet Discreta en el filtrado de señales bioeléctricas. Umbral Científico, 5, 92-98.
Bashashati, A., Fatourechi, M., Ward, R., & Birch, G. (2007). A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. Journal of Neural Engineering, 4(2), R32-R57.
Basher, I. A., & Hajmeer, M. (2000). Artificial neural networks: fundamentals, computing, designg, and application. Journal of Microbiological Methods, 43(1), 3-31.
Bishop, C. M. (2006). Pattern recognition and machine learning. New York, NY: Springer Science + Business Media
Bookheimer, S. (2004). What brain imaging can tell us about developmental disorders [Documento en línea]. Department of Psychiatry and Biobehavioral Sciences, UCLA, School of Medicine. Recuperado de http://www.thehelpgroup.org/pdf/guide/Book_brainimaging.pdf
Burrus, S., Ramesh, A., & Guo, H. (1999). Introduction to Wavelets and Wavelets transforms. New York, NY: Prentice Hall.
Cacioppo, C. J., & Tassinary, L. G. (1990). Inferring physiological significance from physiological signals. American Psychologist,45(1), 16-28
Chandaka, S., Chatterjee, A., & Munshi, S. (2008). Cross-correlation aided support vector machine classifer for classification of EEG signals. Expert Systems with Applications, 36(2), 1329–1336
Chethan, P., & Cox, M. (2002). Frequency characteristics of wavelets. IEEE Transactions on Power Delivery, 17(3), 800-804.
Cookson, C. (2011, Dic.21). A head start with brainwaves [Publicación en ñínea]. Financial Times. Recuperado de http://www.ft.com/cms/s/0/557ff0b0-228e-11e1-8404-00144feabdc0.html#axzz1qqQJqphq.
Cortés, J., Cano, H., & Chaves, J. (2007a). Del análisis de Fourier al las Wavelets: Análisis de Fourier. Scientia et Technica, XIII(34), 151-156.
Cortés, J., Cano, H., & Cháves, J. (2007b). Del análisis de Fourier al análisis Wavelets: transformada continua Wavelet (TCW). Scientia et Technica, XIII(30), 133-138.
Dreiseitl, S., & Ohno-Machado, L. (2002). Logistic regression and artificial neural network classification models: a methodology review. Journal of Biomedical Informatics, 35, 5-6, 352-359.
Fausett, L. (1994). Fundamentals of neural networks architectures, algorithms and applications. New York, NY: Prentice Hall.
Fugal, L. (2009). Conceptual wavelets in digital signal procesing. San Diego, CA: Space & Signals Technologies.
Garcia, G., Tsoneva, T., & Nijholt, A. (2009). Emotional brain-computer interfaces [Documento en línea]. Recuperado de http://hmi.ewi.utwente.nl/abci2009_files/EmotionalBrain-ComputerInterfaces.pdf
García,G., Velandía, R., & Barón, E. (2006). Algoritmo de la reducción de ruido en señales de electroencefalografía utilizando la DWT. Umbral Científico, 8, 34-40. Recuperado de http://redalyc.uaemex.mx/pdf/304/30400805.pdf
Guerrero, J. (2010.). Procesado Digital de Bioseñales [Documento en línea]. Universidad de Valencia. Recuperado de http://ocw.uv.es/ingenieria-y-arquitectura/1-5/ib_material/IB_T4_OCW.pdf
Huaping, J. (2011). Neural network in the application of EEG signal classification method, En 7th International Conference on Computarional Intelligence and Security. Hainan, China, pp. 1325-1327. Los Alamitos, CA: IEEE Computer Society. doi: 10.1109/CIS.2011.294
Jacques, G., Frymiare, J.L. ; Kounios, J. ; Clark, C. ; & Polikar, R. (2005). Multiresolution wavelet analysis and ensemble of classifers for early diagnosis of alzheimer disease. En IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). Vol.5. pp. 389-392. Piscataway, NJ: IEEE.
Jahankhani, P., Kodogiannis, V., & Revett, K. (2006). EEG signal classification using wavelet feature extraction and neural networks. En IEEE John Vincent Atanasoff 2006 International symposium on modern computing 2006 JVA’06, pp 120-124. Los Alamitos, CA: IEEE Computer Society. doi: 10.1109/JVA.2006.17 .
Kandaswamy, A., Kumar, C. S., Ramanathan, R., Jayaraman, S., & Malmurugan, N. (2004). Neural classification of lung sound using wavelet coefficients. Computers on Biology and Medicine, 34(6):523-537.
Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Patras, I. (2011). DEAP: A database for emotion analysis using physiological signals [In press]. IEEE Transactions on Affective Computing. Special Issue on Naturalistic Affect Resources for System Building and Evaluation, 1-15. Recuperado de http://www.eecs.qmul.ac.uk/mmv/datasets/deap/doc/tac_special_issue_2011.pdf
Kotsiantis, S. B. (2007). Supervised machine learning: a review of classification techniques. Informatica, 31, 249-268.
Kousarrizi, M. R.N, Asadi Ghanbari, A., Teshnehlab, M., Aliyari, M., & Gharaviri, A.A, Teshnehlab, M., Aliyari, M., & Gharaviri, A. (2009). Feature extraction and classification of EEG signals using Wavelet transform, SVM and artificial neural networks for brain computer interfaces. En International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, IJCBS ’09, Sahngai, China, pp. 352-355. Los Alamitos, CA: IEEE Computer Society.
Kwiatkowska, J. (2008). Management of Consumer’s Attention – What can the advertiser did to survive the media revolution [Documento en línea]. Czestochowa University of Technology. Recuperado de http://www.oeconomica.uab.ro/upload/lucrari/1020082/61.pdf
Lang, P. J., & Bradley, M. M. (1994). Measuring emotion: The self-assessment manikin and the semantic differential. Journal of Behavioral Therapy and Experimental Psychiatry, 25(1), 49-59. http://www.cnbc.pt/jpmatos/29.%20Bradley.pdf
Learned, R. E., & Willsky, A. S. (1995). A Wavelet Packet Approach to transient signal classification. Applied Computer Harmonic Analysis, 2, 265-278. http://ssg.mit.edu/~willsky/publ_pdfs/129_pub_ACH.pdf
Mallat, S. G. (1989). A theory for multi-resolution signal decomposition: The Wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674-693.
Mukul, M. K., & Matsuno, F. (2009). EEG de-noising based on wavelet transforms and extraction of sub-band components related to movement imagination. En ICAAS-SICE, 2009, Fukuoka, Japón, pp. 1605-1610. Tokyo, Japón: The Society of Instrument and Control Engineers (SICE)
Murugappan, M., Nagarajan, R., & Yaacob, S. (2010). Classification of human emotion from EEG using discrete Wavelet transform. Journal of Biomedical Science and Engineering, 3(4), 390-396. doi: 10.4236/jbise.2010.34054
Murugappan, M., Rizon, M., Nagarajan, R., & Yaacob, S. (2009). An Investigation on visual and audiovisual stimulus based emotion recognition using EEG. International Journal of Medical Engineering and Informatics, 1(3) pp. 342-356. doi: 10.1504/IJMEI.2009.022645
Nakayama, K. (2007). A brain computer interface based on FFT and multilayer neural network - feature extraction and generalization. En Proceedings of 2007 International symposium on intelligent signal processing and communication systems. Xiamen, China, pp. 826-829. IEEE circuit and Science Society. doi: 10.1109/ISPACS.2007.4446015
Nilsson, N. (1998). Introduction to machine learning. Stanford, CA: Standford University
Pardue, J.H., Landry, J.P., & Clark, T.D., Jr. (1995). A soft systems approach to input distribution estimation for a non-stationary demand process. En WSC '95 Proceedings of the 27th conference on Winter simulation, pp. 982-987. Washington, DC: IEEE Computer Society. doi:10.1145/224401.224761
Pei, X. & Zheng, C (2008). Classification of left and right hand motor imagery tasks based on EEG frecuency component selection. En The 2nd International conference on bioinformatics and biomedical engineering,ICBBE 2008, pp. 1888-1891. Piscataway, NJ: IEEE. doi: 10.1109/ICBBE.2008.801
Picard, R. W. (2000). Affective Computing. Cambridge, MA: MIT Press.
Picard, R. W., Vyzas, E., & Healey, J. (2001.). Towards Machine Emotional Intelligence: Analysis of Affective Physiological State. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(10), 1175-1191.
Procházka, A., Kukal, J., & Vysata, O. (2008). Wavelet transform use for feature extraction and EEG signal segments classification. En 3rd International Symposium on Communications, Control and Signal Processing, 2008. ISCCSP 2008, St. Juliens, Malta, pp. 719-722. Piscataway, NJ: IEEE.
Pywavelets (2008-2012). Wavelet daubechies 4 (db4). En Wavelet browser [Sitio Web]. Recuperado de http://wavelets.pybytes.com/wavelet/db4/ http://media.economist.com/sites/default/files/imagecache/full-width/images/print-edition/20111029_STD001_0.jpg
Rangayyan, R. M. (2002). Biomedical signal analysis: A case-study approach. New York, NY: IEEE Press.
Reading the brain. Mind-goggling (2011, Oct. 29). The Economist. Recuperado de http://www.economist.com/node/21534748
Sherwood, J., & Derakhshani, R. (2009). On classifiability of wavelet features for EEG-basedbrain-computer interfaces. En International joint conference on neural networks, 2009. IJCNN 2009, pp. 2895-2902. Piscataway, NJ: IEEE. doi: 10.1109/IJCNN.2009.5178939
Sørensen, J. (2008). Measuring emotions in a consumer decision making context – Approaching or avoiding [working paper] Aalborg University, Dinamarca. Recuperado de http://www.business.aau.dk/wp/08-20.pdf
Subasia, A., & Ercelebi, E. (2005). Classification of EEG signals using neural network and logistic regression. Computer Methods and Programs in Biomedicine, 78(2), 87-89.
Vélez, P., & Saldarriaga, H. (2010). Clasificación básica de neuroseñales [Tesis de maestría]. Universidad Tecnológica de Pereira.
Windhorst, U. (1999). Modern Techniques in Neuroscience Research. Berlín, Alemania: Springer Verlag.
Wolpaw, J., Birbaumer, N., McFarland, D., Pfurtscheller, G., & Vaughan, T. (2002.). Brain computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767-791.
Young, C. (2002). Brain waves, picture sorts, and branding moments. Journal of Advertising Research, 42(4). 42-53
Yu, L. (2009). EEG De-Noising Based on Wavelet Transformation. En 3rd International Conference on Bioinformatics and Biomedical Engineering. Beijing, China, pp. 1-4. New York, NY: Curran. doi: 10.1109/ICBBE.2009.5162680
Zhang, X., Yin, L., & Wang, W. (2011). Wavelet Time-frecuency Analysus of Electro- encephalogram (EEG) Processing. International Journal of Advanced Computer Science and Applications (IJACSA), 1(5), 1-5.
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2012-03-31
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Esta publicación está licenciada bajo los términos de la licencia CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/deed.es)
