Estudio comparativo de métodos para el reconocimiento de potenciales relacionados a eventos P300 para una interfaz cerebro-computador

In recent years, the Brain Computer Interfaces(BCI) have been highly studied, due to they allow to interact with the environment without the requirement to use the peripherical nervious system. Consequently, The appliaction of this, has been very useful in the rehabilitation engineering. However, th...

Full description

Autores:
Blanco Díaz, Cristian Felipe
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2020
Institución:
Universidad Antonio Nariño
Repositorio:
Repositorio UAN
Idioma:
spa
OAI Identifier:
oai:repositorio.uan.edu.co:123456789/2215
Acceso en línea:
http://repositorio.uan.edu.co/handle/123456789/2215
Palabra clave:
Interfaz Cerebro-Computador
Electroencefalografía
Potencial Relacionado a Eventos
P300
Análisis de Correlación Canónica
Brain-Computer Interface
Electroencephalography
Event-Related Potential
P300
Canonical Correlation Analysis
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
id UAntonioN2_1ec14719b5c3004a5722b79841ae7045
oai_identifier_str oai:repositorio.uan.edu.co:123456789/2215
network_acronym_str UAntonioN2
network_name_str Repositorio UAN
repository_id_str
dc.title.es_ES.fl_str_mv Estudio comparativo de métodos para el reconocimiento de potenciales relacionados a eventos P300 para una interfaz cerebro-computador
title Estudio comparativo de métodos para el reconocimiento de potenciales relacionados a eventos P300 para una interfaz cerebro-computador
spellingShingle Estudio comparativo de métodos para el reconocimiento de potenciales relacionados a eventos P300 para una interfaz cerebro-computador
Interfaz Cerebro-Computador
Electroencefalografía
Potencial Relacionado a Eventos
P300
Análisis de Correlación Canónica
Brain-Computer Interface
Electroencephalography
Event-Related Potential
P300
Canonical Correlation Analysis
title_short Estudio comparativo de métodos para el reconocimiento de potenciales relacionados a eventos P300 para una interfaz cerebro-computador
title_full Estudio comparativo de métodos para el reconocimiento de potenciales relacionados a eventos P300 para una interfaz cerebro-computador
title_fullStr Estudio comparativo de métodos para el reconocimiento de potenciales relacionados a eventos P300 para una interfaz cerebro-computador
title_full_unstemmed Estudio comparativo de métodos para el reconocimiento de potenciales relacionados a eventos P300 para una interfaz cerebro-computador
title_sort Estudio comparativo de métodos para el reconocimiento de potenciales relacionados a eventos P300 para una interfaz cerebro-computador
dc.creator.fl_str_mv Blanco Díaz, Cristian Felipe
dc.contributor.advisor.spa.fl_str_mv Ruiz Olaya, Andrés Felipe
dc.contributor.author.spa.fl_str_mv Blanco Díaz, Cristian Felipe
dc.subject.es_ES.fl_str_mv Interfaz Cerebro-Computador
Electroencefalografía
Potencial Relacionado a Eventos
P300
Análisis de Correlación Canónica
topic Interfaz Cerebro-Computador
Electroencefalografía
Potencial Relacionado a Eventos
P300
Análisis de Correlación Canónica
Brain-Computer Interface
Electroencephalography
Event-Related Potential
P300
Canonical Correlation Analysis
dc.subject.keyword.es_ES.fl_str_mv Brain-Computer Interface
Electroencephalography
Event-Related Potential
P300
Canonical Correlation Analysis
description In recent years, the Brain Computer Interfaces(BCI) have been highly studied, due to they allow to interact with the environment without the requirement to use the peripherical nervious system. Consequently, The appliaction of this, has been very useful in the rehabilitation engineering. However, the traslation of the user's intent through of Electroencephalography(EEG) is still a challenge for the scientific community, consequently, the stimulation that allow to evoke responses in patterns form, for that the system can recognizes them, is necessary. An experiment highly used corresponding to the Oddball paradigm, that through of visual stimulus, allow to evoke a positive deflection in the parieto-central cortex to the 300 ms, when the subject is interested in a specific stimuli between aleatory stimulation, known as P300 potential. The P300 have a problematic in his recognition that consist in a low signal to noise ratio, this generate that the extraction techniques be reason of interest. In the present work, a comparative study between five P300-recognition methods is preformed: two standard methods reported in literature: Mean-Amplitude-LDA(MA-LDA) and Stepwise-LDA(SWLDA), and three novel methods based in the Canonical Correlation Analysis(CCA): MA+CCA-LDA, CCA with Regularizad Logistic Regression and CCA with Multilayer Perceptron(MLP). The methods were validated in a available dataset, that consisted in a BCI-P300 system implemented in a Speller. Using as evaluation metrics: the classification percentage and the computational cost. Also a measurement protocol in healthy people was developed, to implement the BCI-P300 Speller in real time, at the simulation Lab of the Universidad Antonio Nariño, using the device of EEG acquisition g.Nautilus-32 PRO and the public software BCI 2000
publishDate 2020
dc.date.issued.spa.fl_str_mv 2020-06-02
dc.date.accessioned.none.fl_str_mv 2021-03-02T14:24:14Z
dc.date.available.none.fl_str_mv 2021-03-02T14:24:14Z
dc.type.spa.fl_str_mv Trabajo de grado (Pregrado y/o Especialización)
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
dc.type.coarversion.none.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
format http://purl.org/coar/resource_type/c_7a1f
dc.identifier.uri.none.fl_str_mv http://repositorio.uan.edu.co/handle/123456789/2215
dc.identifier.bibliographicCitation.spa.fl_str_mv Abdulkader, S., Atia, A., Mostafa, S., y Mostafa, M. (2015). Brain computer interfacing: Applications and challenges. Egyptian Informatic Journal , 16 , 213-230.
Abiri, R., Borhani, S., Sellers, E., Jiang, Y., y Zhao, X. (2018, 11). A comprehensive review of EEG-based brain-computer interface paradigms. Journal of Neural Engineering, 16 .
Al-Fahoum, A., y Al-Fraihat, A. (2014). Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains. ISRN Neuroscience, 1-7.
Ameera, A., A.Saidatul, y Ibrahim, Z. (2018). Analysis of EEG spectrum bands using power spectral density for pleasure and displeasure state. En IOP conference series: Materials science and engineering.
Asociación Médica Mundial, A. (1964). Declaración de helsinki.
Bai, O., Kelly, G., Fei, D., Murphy, D., Fox, J., Burkhardt, B., . . . Soars, J. (2015). A wireless, smart EEG system for volitional control of lower-limb prosthesis. En Tencon 2015 - 2015 IEEE region 10 conference (p. 1-6).
Basar, E., y A.Duzgun. (2015). The clair model: Extention of brondmanns areas based on brain oscillations and connectivity. International Journal of Psychophysiology, 103 , 185-198.
Baura, G. (2011). Medical device technologies:a system-based overview using engineering standars. San Diego(CA): Oxford Academic Press.
Becedas, J. (2012). Brain–machine interfaces: Basis and advances. IEEE Transactions on Systems, Man and Cybernetics-Part C: Applications and Reviews, 42 (6), 825-836.
Blanco, C., y Ruiz, A. (2020). Caracterización de señales de EEG relacionadas a potenciales evocados visuales en estado estacionario. Revista Ontare, 7 .
Boelts, J., Cerquera, A., y Ruiz, A. (2015). Decoding of imaginary motor movements of fists applying spatial filtering in a BCI simulated application. En International work-conference on the interplay between natural and artificial computation ( IWINAC 2015). Elche, Spain.
Bolduc-Teasdale, J., Jolicoeur, P., y McKerral, M. (2012). Multiple electrophysiological markers of visual-attentional processing in a novel task directed toward clinical use. Journal of ophthalmology , 2012 , 618-654.
Bright, D., Nair, A., Salvekar, D., y Bhisikar, S. (2016). EEG-based brain controlled prost- hetic arm. En 2016 conference on advances in signal processing (CASP). Pune, India. Brouwer, A., y Erp, J. V. (2010). A tactile P300 brain-computer interface. Frontier in Neuroscience, 4 , 1-19.
Caicedo, E., y López, J. (2009). Una aproximación práctica a las redes neuronales. Cali: Programa editorial universidad del Valle.
Cecotti, H., y Graeser, A. (2011). Convolutional neural networks for P300 detection with application to brain-computer interfaces. IEEE transactions on pattern analysis machine intelligence, 33 , 433-45.
Chaudary, U., Birbaumer, N., y Ramos, A. (2016). Brain-computer interfaces for communi- cation and rehabilitation. Nature Reviews Neurology, 12 , 513-525.
Chiou, E., y Puthusserypady, S. (2016). Filter feature extraction methods for P300 BCI speller: A comparison. En 2016 IEEE international conference on systems, man, and cybernetics. Budapest, Hungary.
Elsawy, A., Eldawlatly, S., Taher, M., y Aly, G. (2013). A principal component analysis ensemble classifier for P300 speller applications. En 8th international symposium on image and signal processing and analysis (ISPA).
Farwell, L., y Donchin, E. (1988). Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and clinical Neurophysiology, 70 , 510-523.
Fazel, R., y Abhari, K. (2009). A region-based P300 speller for brain-computer interface. Electrical and Computer Engineering, Canadian Journal of , 34 , 81 - 85.
Frolov, A., Mokienko, O., Lyukmanov, E. K. S., R. and Biryukova, Lydia, T., Georgy, N., y Yulia, B. (2017). Post-stroke rehabilitation training with a motor-imagery-based brain-computer interface (BCI)-controlled hand exoskeleton: A randomized controlled multicenter trial. Frontiers in Neuroscience, 11 , 400.
Goméz, J., y Departamento Administrativo Nacional de Estadística, D. (2008). Discapacidad. url https://www.dane.gov.co/index.php/estadisticas-por-tema/demografia-y- poblacion/discapacidad.
g.tec medical engineering Gmbh. (2017). Instruction for use v1.16.06 g.nautilus pro [Manual de software informático]. Schiedlberg.
Gupta, S., y Singh, H. (1996). Preprocessing EEG signals for direct human-system interface. En Proceedings IEEE international joint symposia on intelligence and systems
Gámez Albán, H., Cabrera, J., Salas, O., y Bravo Bastidas, J. (2016). Aplicación de mapas de kohonen para la priorización de zonas de mercado: una aproximación práctica. Revista EIA, 13 , 157-169.
Haider, A., y Fazel, R. (2017). Chapter2:applications of p300 event related potential in brain computer interface. Croatia: Oxford Academic Press.
Hastie, T., Tibshirani, R., y Friedman, J. (2009). The elements of statistical learning. New York (NY): Springer.
Hohne, J., Tangermann, M., y Towards, M. (2014). User-friendly spelling with an auditory Brain Computer Interface: The charstreamer paradigm. Plos ONE , 9 .
Hwang, J., Lee, M., y Lee, S. (2017). A brain-computer interface speller using peripheralstimulus-based SSVEP and P300. En 5 th international winter conferen- ce on brain-computer interface(BCI). Sabuk, South Korea.
Kabbara, A., Khalil, M., El-Falou, W., Eid, H., y Hassan, M. (2015). Functional brain connectivity as a new feature for P300 speller. PLOS One, 11 , 1-18.
Karimi, S., Mijani, A., Talebian, M., y Mirzakuchaki, M. (2019). Comparison of the P300 detection accuracy related to the BCI speller and image recognition scenario. Arxiv , 1912.11371 .
Ko-lodziej, M., Majkowski, A., y Rak, R. (2010). Matlab FE-toolbox - an universal utility for feature extraction of EEG signals for BCI realization. Przeglpmd Elektrotechniczny , 86 , 44-46.
Krusienski, D., Sellers, E., Cabestaing, F., Bayoudh, S., Mcfarland, D., Vaughan, T., y Wolpaw, J. (2007). A comparison of classification techniques for the P300 speller. Journal of neural engineering , 3 , 299-305.
Krusienski, D., Sellers, E., Cabestaing, F., Bayoudh, S., Mcfarland, D., Vaughan, T., y Wol- paw, J. (2008). Toward enhanced P300 speller performance. Journal of neuroscience methods, 167 , 15-21.
Kumar, J., y Bhuvaneswari, P. (2012). Analysis of electroencephalography (EEG) signals and its categorization–a study. Clinical Neurophysiology, 38 , 2525-2536.
Kwak, N., Mu¨ller, K., y Lee, S. (2017). A convolutional neural network for steady state visual evoked potential classification under ambulatory environment. PLOS ONE , 12 (2), 1-20.
Lee, H., Kwon, Y., Kim, Y., Kim, H., Lee, Y., Williamson, J., . . . Lee, S. (2019). EEG dataset and open BMI toolbox for three BCI paradigms: an investigation into BCI illiteracy. Giga-Science, 8 , 1-16.
Li, F., Yi, C., Jiang, Y., Liao, Y., Si, Y., Dai, J., . . . Xu, P. (2019). Different contexts in the oddball paradigm induce distinct brain networks in generating the P300. Frontiers in Human Neuroscience, 12 , 520.
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dc.identifier.instname.spa.fl_str_mv instname:Universidad Antonio Nariño
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional UAN
dc.identifier.repourl.spa.fl_str_mv repourl:https://repositorio.uan.edu.co/
url http://repositorio.uan.edu.co/handle/123456789/2215
identifier_str_mv Abdulkader, S., Atia, A., Mostafa, S., y Mostafa, M. (2015). Brain computer interfacing: Applications and challenges. Egyptian Informatic Journal , 16 , 213-230.
Abiri, R., Borhani, S., Sellers, E., Jiang, Y., y Zhao, X. (2018, 11). A comprehensive review of EEG-based brain-computer interface paradigms. Journal of Neural Engineering, 16 .
Al-Fahoum, A., y Al-Fraihat, A. (2014). Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains. ISRN Neuroscience, 1-7.
Ameera, A., A.Saidatul, y Ibrahim, Z. (2018). Analysis of EEG spectrum bands using power spectral density for pleasure and displeasure state. En IOP conference series: Materials science and engineering.
Asociación Médica Mundial, A. (1964). Declaración de helsinki.
Bai, O., Kelly, G., Fei, D., Murphy, D., Fox, J., Burkhardt, B., . . . Soars, J. (2015). A wireless, smart EEG system for volitional control of lower-limb prosthesis. En Tencon 2015 - 2015 IEEE region 10 conference (p. 1-6).
Basar, E., y A.Duzgun. (2015). The clair model: Extention of brondmanns areas based on brain oscillations and connectivity. International Journal of Psychophysiology, 103 , 185-198.
Baura, G. (2011). Medical device technologies:a system-based overview using engineering standars. San Diego(CA): Oxford Academic Press.
Becedas, J. (2012). Brain–machine interfaces: Basis and advances. IEEE Transactions on Systems, Man and Cybernetics-Part C: Applications and Reviews, 42 (6), 825-836.
Blanco, C., y Ruiz, A. (2020). Caracterización de señales de EEG relacionadas a potenciales evocados visuales en estado estacionario. Revista Ontare, 7 .
Boelts, J., Cerquera, A., y Ruiz, A. (2015). Decoding of imaginary motor movements of fists applying spatial filtering in a BCI simulated application. En International work-conference on the interplay between natural and artificial computation ( IWINAC 2015). Elche, Spain.
Bolduc-Teasdale, J., Jolicoeur, P., y McKerral, M. (2012). Multiple electrophysiological markers of visual-attentional processing in a novel task directed toward clinical use. Journal of ophthalmology , 2012 , 618-654.
Bright, D., Nair, A., Salvekar, D., y Bhisikar, S. (2016). EEG-based brain controlled prost- hetic arm. En 2016 conference on advances in signal processing (CASP). Pune, India. Brouwer, A., y Erp, J. V. (2010). A tactile P300 brain-computer interface. Frontier in Neuroscience, 4 , 1-19.
Caicedo, E., y López, J. (2009). Una aproximación práctica a las redes neuronales. Cali: Programa editorial universidad del Valle.
Cecotti, H., y Graeser, A. (2011). Convolutional neural networks for P300 detection with application to brain-computer interfaces. IEEE transactions on pattern analysis machine intelligence, 33 , 433-45.
Chaudary, U., Birbaumer, N., y Ramos, A. (2016). Brain-computer interfaces for communi- cation and rehabilitation. Nature Reviews Neurology, 12 , 513-525.
Chiou, E., y Puthusserypady, S. (2016). Filter feature extraction methods for P300 BCI speller: A comparison. En 2016 IEEE international conference on systems, man, and cybernetics. Budapest, Hungary.
Elsawy, A., Eldawlatly, S., Taher, M., y Aly, G. (2013). A principal component analysis ensemble classifier for P300 speller applications. En 8th international symposium on image and signal processing and analysis (ISPA).
Farwell, L., y Donchin, E. (1988). Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and clinical Neurophysiology, 70 , 510-523.
Fazel, R., y Abhari, K. (2009). A region-based P300 speller for brain-computer interface. Electrical and Computer Engineering, Canadian Journal of , 34 , 81 - 85.
Frolov, A., Mokienko, O., Lyukmanov, E. K. S., R. and Biryukova, Lydia, T., Georgy, N., y Yulia, B. (2017). Post-stroke rehabilitation training with a motor-imagery-based brain-computer interface (BCI)-controlled hand exoskeleton: A randomized controlled multicenter trial. Frontiers in Neuroscience, 11 , 400.
Goméz, J., y Departamento Administrativo Nacional de Estadística, D. (2008). Discapacidad. url https://www.dane.gov.co/index.php/estadisticas-por-tema/demografia-y- poblacion/discapacidad.
g.tec medical engineering Gmbh. (2017). Instruction for use v1.16.06 g.nautilus pro [Manual de software informático]. Schiedlberg.
Gupta, S., y Singh, H. (1996). Preprocessing EEG signals for direct human-system interface. En Proceedings IEEE international joint symposia on intelligence and systems
Gámez Albán, H., Cabrera, J., Salas, O., y Bravo Bastidas, J. (2016). Aplicación de mapas de kohonen para la priorización de zonas de mercado: una aproximación práctica. Revista EIA, 13 , 157-169.
Haider, A., y Fazel, R. (2017). Chapter2:applications of p300 event related potential in brain computer interface. Croatia: Oxford Academic Press.
Hastie, T., Tibshirani, R., y Friedman, J. (2009). The elements of statistical learning. New York (NY): Springer.
Hohne, J., Tangermann, M., y Towards, M. (2014). User-friendly spelling with an auditory Brain Computer Interface: The charstreamer paradigm. Plos ONE , 9 .
Hwang, J., Lee, M., y Lee, S. (2017). A brain-computer interface speller using peripheralstimulus-based SSVEP and P300. En 5 th international winter conferen- ce on brain-computer interface(BCI). Sabuk, South Korea.
Kabbara, A., Khalil, M., El-Falou, W., Eid, H., y Hassan, M. (2015). Functional brain connectivity as a new feature for P300 speller. PLOS One, 11 , 1-18.
Karimi, S., Mijani, A., Talebian, M., y Mirzakuchaki, M. (2019). Comparison of the P300 detection accuracy related to the BCI speller and image recognition scenario. Arxiv , 1912.11371 .
Ko-lodziej, M., Majkowski, A., y Rak, R. (2010). Matlab FE-toolbox - an universal utility for feature extraction of EEG signals for BCI realization. Przeglpmd Elektrotechniczny , 86 , 44-46.
Krusienski, D., Sellers, E., Cabestaing, F., Bayoudh, S., Mcfarland, D., Vaughan, T., y Wolpaw, J. (2007). A comparison of classification techniques for the P300 speller. Journal of neural engineering , 3 , 299-305.
Krusienski, D., Sellers, E., Cabestaing, F., Bayoudh, S., Mcfarland, D., Vaughan, T., y Wol- paw, J. (2008). Toward enhanced P300 speller performance. Journal of neuroscience methods, 167 , 15-21.
Kumar, J., y Bhuvaneswari, P. (2012). Analysis of electroencephalography (EEG) signals and its categorization–a study. Clinical Neurophysiology, 38 , 2525-2536.
Kwak, N., Mu¨ller, K., y Lee, S. (2017). A convolutional neural network for steady state visual evoked potential classification under ambulatory environment. PLOS ONE , 12 (2), 1-20.
Lee, H., Kwon, Y., Kim, Y., Kim, H., Lee, Y., Williamson, J., . . . Lee, S. (2019). EEG dataset and open BMI toolbox for three BCI paradigms: an investigation into BCI illiteracy. Giga-Science, 8 , 1-16.
Li, F., Yi, C., Jiang, Y., Liao, Y., Si, Y., Dai, J., . . . Xu, P. (2019). Different contexts in the oddball paradigm induce distinct brain networks in generating the P300. Frontiers in Human Neuroscience, 12 , 520.
Li, J., Gu, R., Ji, H., Pang, Z., y Li, M. (2016). Interaction study of SSVEP and P300 in electroencephalogram. En Paper presented at: Proceedings of the PIERS Progress in Electromagnetic Research Symposium. Shangai, China.
Materka, A., y Poryzala, P. (2014). A robust asynchronous ssvep brain-computer interface based on cluster analysis of canonical correlation coefficients. Advances in Intelligent Systems and Computing , 300 , 3-14.
McFarland, D., y Wolpaw, J. (2011). Brain-computer interfaces for communication and control. Communications of the ACM , 5 , 60-66.
Mendoza, O. (2017). Development of a Hybrid Brain-Computer Interface for Autonomous System (Tesis Doctoral no publicada). Free University of Berlin.
Ministerio de salud, M. (1993). Resolución número 8430 de 1993.
Motlagh, E., y Ibrahim, F. (2015). Developing an optimized single-trial P300-based brain computer interface system. En International Conference for Innovation in Biomedical Engineering and Life Sciences. Putrajaya, Malaysia.
Muller-Putz, G., y Pfurtscheller, G. (2008). Control of an electrical prosthesis with an SSVEP-based BCI. IEEE Transactions on Biomedical Engineering, 55 , 361 - 364.
Murphy, D., Bai, O., Gorgey, O., Fox, A., Lovegreen, J., William, T., . . . Fei, D. (2017). Electroencephalogram-based brain–computer interface and lower-limb prosthesis control: A case study. Frontiers in Neurology , 8 , 696.
Nacional Center for Adaptative Neurotechnologies, N. (2018). BCI2000. url https://www. bci2000.org/mediawiki/index.php/MainP age.
Nicolas, L., y Gomez, J. (2012). Brain computer interfaces, a review. Sensors, 12 (6), 1211-1279.
Niedermeyer, E., y da Silva, F. (2004). Electroencephalography:basic principles, clinical applications and related fields. Philadelphia(PA): Lippincot Williams Wilkins.
Ono, T., Shindo, K., Kawashima, K., Ota, N., Ito, M., Ota, T., . . . Ushiba, J. (2014). Brain-computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke. Frontiers in Neuroengineering , 7 , 19.
Orellana, D., y Cuenca, J. (2017). Comparative study of feature extraction methods and classificationof event-related potentials P300. CEDAMAZ , 7 , 71-82.
Patelia, V., y Patel, M. (2019). Brain computer interface: Applications and P300 over- view. En 10th ICCCNT International Conference on Computing, Communication and Networking Technologies. Kanpur, India.
Piccione, F., Giorgi, F., Tonin, P., Priftis, K., Giove, S., Silvoni, S., . . . Beverina, F. (2006). P300-based brain computer interface: Reliability and performance in healthy and pa- ralysed participants. Clinical Neurophysiology, 117 (3), 531 - 537.
Picton, T. (1992). The P300 a wave of the human event-related potential. Journal of Clinical Neurophysiology, 9 , 456-479.
Pugh, G. (1977). The biological origin of human values. Michigan(MI): Basic Books.
Raksha, N., Sahana, S., Sahana, P., y Niranjana, K. (2018). Stepwise and quadratic discrimi- nant analysis of P300 signals for controlling a robot. En 2018 international conference on networking, embedded and wireless system (ICNEWS). Bangalore, India.
Rezeika, A., Benda, M., Stawicki, P., Gembler, F., Saboor, F., y Volosyak, I. (2018). Brain- computerinterface spellers: A review. Brain Sciences, 8 , 1-38.
Seeck, M., Koessler, L., Bast, T., Leijten, F., Michel, C., Baumgartner, C., . . . Beniczky, S. (2017). The standardized EEG electrode array of the IFCN. Clinical Neurophysiology, 128 (10), 2070-2077.
Shalev, S., y Ben, D. (2014). Understanding machine learning: From theory to algorithms (draft ed.). CUP.
Spuler, M., Walter, A., Rosenstiei, W., y Bogdan, M. (2014). Spatial filtering based on canonical correlation analysis for classification of evoked or event-related potentials in EEG data. IEEE transactions on neural systems and rehabilitation engineering., 22 , 1097-1103.
Sur, S., y Sinha, V. (2009). Event-related potential: An overview. Ind Psychiatry J , 18 (1), 70-73.
Tal, O., y D.Friedman. (2019). Recurrent neural networks for P300-based BCI. arXiv .
Tang, J., Liu, Y., Hu, D., y Zhou, Z. (2018). Towards BCI-actuated smart wheelchair system. Biomedical engineering online, 17 , 111.
Viana, S., Batista, D., y Melges, D. (2014). Logistic regression models: Feature selection for P300 detection improvement. En XXIV congresso brasileiro de engenharia biom´edica- CBEB. Belo Horizonte, Brazil.
Villamizar, N. (2019). Elaboración y ejecución de un protocolo para adquisición de señales de electroencefalografía para una interfaz cerebro-computadora, aplicado a ingeniería de rehabilitación. Bogotá: Universidad Antonio Nariño.
Wang, H., Wang, Y., Jing, J., y Wang, X. (2015). SSVEP recognition using multivariate linearregression for brain computer interface. En IEEE international conference on computer and communications(ICCC). Chengdu, China.
Wang, H., Zhang, Y., Waytowich, N., Krusienski, D., Zhou, G., Jin, J., . . . Chichocki, A. (2016). Discriminative feature extraction via multivariate linear regression for SSVEP- based BCI. IEEE transactions on neural systems and rehabilitation engineering., 24 , 1-10.
World Health Organization. (2011). World report on disability . Geneve. (ISBN 9789241564182)
Xiao, X., Xu, M., Jin, J., Wang, Y., Jung, T., y Ming, D. (2019). Discriminative canonical pattern matching for single-trial classification of ERP components. IEEE Transaction on BiomedicalEngineering , 1-1.
Xiao, X., Xu, M., Wang, Y., Jung, T., y Ming, D. (2019). comparison of classification methods for recognizing single-trial P300 in brain-computer interfaces. En 41st an- nual international conference of the ieee engineering in medicine and biology society. Berlin, Germany.
Xu, M., Long, C., y He, F. (2016). Incorporation of inter-subject information to improve the accuracy of subject-specified P300 classifiers. International Journal of Neural Systems, 26 , 1-10.
Zhang, X., Xu, G., Mou, X., Ravi, A., Li, M., Wang, Y., y Jiang, N. (2019). A convolutional neural network for the detection of asynchronous steady state motion visual evoked po- tential. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27 (6), 1303-1311.
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spelling Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)Acceso abiertohttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Ruiz Olaya, Andrés FelipeBlanco Díaz, Cristian Felipe2021-03-02T14:24:14Z2021-03-02T14:24:14Z2020-06-02http://repositorio.uan.edu.co/handle/123456789/2215Abdulkader, S., Atia, A., Mostafa, S., y Mostafa, M. (2015). Brain computer interfacing: Applications and challenges. Egyptian Informatic Journal , 16 , 213-230.Abiri, R., Borhani, S., Sellers, E., Jiang, Y., y Zhao, X. (2018, 11). A comprehensive review of EEG-based brain-computer interface paradigms. Journal of Neural Engineering, 16 .Al-Fahoum, A., y Al-Fraihat, A. (2014). Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains. ISRN Neuroscience, 1-7.Ameera, A., A.Saidatul, y Ibrahim, Z. (2018). 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Electroencephalography and clinical Neurophysiology, 70 , 510-523.Fazel, R., y Abhari, K. (2009). A region-based P300 speller for brain-computer interface. Electrical and Computer Engineering, Canadian Journal of , 34 , 81 - 85.Frolov, A., Mokienko, O., Lyukmanov, E. K. S., R. and Biryukova, Lydia, T., Georgy, N., y Yulia, B. (2017). Post-stroke rehabilitation training with a motor-imagery-based brain-computer interface (BCI)-controlled hand exoskeleton: A randomized controlled multicenter trial. Frontiers in Neuroscience, 11 , 400.Goméz, J., y Departamento Administrativo Nacional de Estadística, D. (2008). Discapacidad. url https://www.dane.gov.co/index.php/estadisticas-por-tema/demografia-y- poblacion/discapacidad.g.tec medical engineering Gmbh. (2017). Instruction for use v1.16.06 g.nautilus pro [Manual de software informático]. Schiedlberg.Gupta, S., y Singh, H. (1996). Preprocessing EEG signals for direct human-system interface. En Proceedings IEEE international joint symposia on intelligence and systemsGámez Albán, H., Cabrera, J., Salas, O., y Bravo Bastidas, J. (2016). Aplicación de mapas de kohonen para la priorización de zonas de mercado: una aproximación práctica. Revista EIA, 13 , 157-169.Haider, A., y Fazel, R. (2017). Chapter2:applications of p300 event related potential in brain computer interface. Croatia: Oxford Academic Press.Hastie, T., Tibshirani, R., y Friedman, J. (2009). The elements of statistical learning. New York (NY): Springer.Hohne, J., Tangermann, M., y Towards, M. (2014). User-friendly spelling with an auditory Brain Computer Interface: The charstreamer paradigm. Plos ONE , 9 .Hwang, J., Lee, M., y Lee, S. (2017). A brain-computer interface speller using peripheralstimulus-based SSVEP and P300. En 5 th international winter conferen- ce on brain-computer interface(BCI). Sabuk, South Korea.Kabbara, A., Khalil, M., El-Falou, W., Eid, H., y Hassan, M. (2015). Functional brain connectivity as a new feature for P300 speller. PLOS One, 11 , 1-18.Karimi, S., Mijani, A., Talebian, M., y Mirzakuchaki, M. (2019). Comparison of the P300 detection accuracy related to the BCI speller and image recognition scenario. Arxiv , 1912.11371 .Ko-lodziej, M., Majkowski, A., y Rak, R. (2010). Matlab FE-toolbox - an universal utility for feature extraction of EEG signals for BCI realization. Przeglpmd Elektrotechniczny , 86 , 44-46.Krusienski, D., Sellers, E., Cabestaing, F., Bayoudh, S., Mcfarland, D., Vaughan, T., y Wolpaw, J. (2007). A comparison of classification techniques for the P300 speller. Journal of neural engineering , 3 , 299-305.Krusienski, D., Sellers, E., Cabestaing, F., Bayoudh, S., Mcfarland, D., Vaughan, T., y Wol- paw, J. (2008). Toward enhanced P300 speller performance. Journal of neuroscience methods, 167 , 15-21.Kumar, J., y Bhuvaneswari, P. (2012). Analysis of electroencephalography (EEG) signals and its categorization–a study. Clinical Neurophysiology, 38 , 2525-2536.Kwak, N., Mu¨ller, K., y Lee, S. (2017). A convolutional neural network for steady state visual evoked potential classification under ambulatory environment. PLOS ONE , 12 (2), 1-20.Lee, H., Kwon, Y., Kim, Y., Kim, H., Lee, Y., Williamson, J., . . . Lee, S. (2019). EEG dataset and open BMI toolbox for three BCI paradigms: an investigation into BCI illiteracy. Giga-Science, 8 , 1-16.Li, F., Yi, C., Jiang, Y., Liao, Y., Si, Y., Dai, J., . . . Xu, P. (2019). Different contexts in the oddball paradigm induce distinct brain networks in generating the P300. Frontiers in Human Neuroscience, 12 , 520.Li, J., Gu, R., Ji, H., Pang, Z., y Li, M. (2016). Interaction study of SSVEP and P300 in electroencephalogram. En Paper presented at: Proceedings of the PIERS Progress in Electromagnetic Research Symposium. Shangai, China.Materka, A., y Poryzala, P. (2014). A robust asynchronous ssvep brain-computer interface based on cluster analysis of canonical correlation coefficients. Advances in Intelligent Systems and Computing , 300 , 3-14.McFarland, D., y Wolpaw, J. (2011). Brain-computer interfaces for communication and control. Communications of the ACM , 5 , 60-66.Mendoza, O. (2017). Development of a Hybrid Brain-Computer Interface for Autonomous System (Tesis Doctoral no publicada). Free University of Berlin.Ministerio de salud, M. (1993). Resolución número 8430 de 1993.Motlagh, E., y Ibrahim, F. (2015). Developing an optimized single-trial P300-based brain computer interface system. En International Conference for Innovation in Biomedical Engineering and Life Sciences. Putrajaya, Malaysia.Muller-Putz, G., y Pfurtscheller, G. (2008). Control of an electrical prosthesis with an SSVEP-based BCI. IEEE Transactions on Biomedical Engineering, 55 , 361 - 364.Murphy, D., Bai, O., Gorgey, O., Fox, A., Lovegreen, J., William, T., . . . Fei, D. (2017). Electroencephalogram-based brain–computer interface and lower-limb prosthesis control: A case study. Frontiers in Neurology , 8 , 696.Nacional Center for Adaptative Neurotechnologies, N. (2018). BCI2000. url https://www. bci2000.org/mediawiki/index.php/MainP age.Nicolas, L., y Gomez, J. (2012). Brain computer interfaces, a review. Sensors, 12 (6), 1211-1279.Niedermeyer, E., y da Silva, F. (2004). Electroencephalography:basic principles, clinical applications and related fields. Philadelphia(PA): Lippincot Williams Wilkins.Ono, T., Shindo, K., Kawashima, K., Ota, N., Ito, M., Ota, T., . . . Ushiba, J. (2014). Brain-computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke. Frontiers in Neuroengineering , 7 , 19.Orellana, D., y Cuenca, J. (2017). Comparative study of feature extraction methods and classificationof event-related potentials P300. CEDAMAZ , 7 , 71-82.Patelia, V., y Patel, M. (2019). Brain computer interface: Applications and P300 over- view. En 10th ICCCNT International Conference on Computing, Communication and Networking Technologies. Kanpur, India.Piccione, F., Giorgi, F., Tonin, P., Priftis, K., Giove, S., Silvoni, S., . . . Beverina, F. (2006). P300-based brain computer interface: Reliability and performance in healthy and pa- ralysed participants. Clinical Neurophysiology, 117 (3), 531 - 537.Picton, T. (1992). The P300 a wave of the human event-related potential. Journal of Clinical Neurophysiology, 9 , 456-479.Pugh, G. (1977). The biological origin of human values. Michigan(MI): Basic Books.Raksha, N., Sahana, S., Sahana, P., y Niranjana, K. (2018). Stepwise and quadratic discrimi- nant analysis of P300 signals for controlling a robot. En 2018 international conference on networking, embedded and wireless system (ICNEWS). Bangalore, India.Rezeika, A., Benda, M., Stawicki, P., Gembler, F., Saboor, F., y Volosyak, I. (2018). Brain- computerinterface spellers: A review. Brain Sciences, 8 , 1-38.Seeck, M., Koessler, L., Bast, T., Leijten, F., Michel, C., Baumgartner, C., . . . Beniczky, S. (2017). The standardized EEG electrode array of the IFCN. Clinical Neurophysiology, 128 (10), 2070-2077.Shalev, S., y Ben, D. (2014). Understanding machine learning: From theory to algorithms (draft ed.). CUP.Spuler, M., Walter, A., Rosenstiei, W., y Bogdan, M. (2014). Spatial filtering based on canonical correlation analysis for classification of evoked or event-related potentials in EEG data. IEEE transactions on neural systems and rehabilitation engineering., 22 , 1097-1103.Sur, S., y Sinha, V. (2009). Event-related potential: An overview. Ind Psychiatry J , 18 (1), 70-73.Tal, O., y D.Friedman. (2019). Recurrent neural networks for P300-based BCI. arXiv .Tang, J., Liu, Y., Hu, D., y Zhou, Z. (2018). Towards BCI-actuated smart wheelchair system. Biomedical engineering online, 17 , 111.Viana, S., Batista, D., y Melges, D. (2014). Logistic regression models: Feature selection for P300 detection improvement. En XXIV congresso brasileiro de engenharia biom´edica- CBEB. Belo Horizonte, Brazil.Villamizar, N. (2019). Elaboración y ejecución de un protocolo para adquisición de señales de electroencefalografía para una interfaz cerebro-computadora, aplicado a ingeniería de rehabilitación. Bogotá: Universidad Antonio Nariño.Wang, H., Wang, Y., Jing, J., y Wang, X. (2015). SSVEP recognition using multivariate linearregression for brain computer interface. En IEEE international conference on computer and communications(ICCC). Chengdu, China.Wang, H., Zhang, Y., Waytowich, N., Krusienski, D., Zhou, G., Jin, J., . . . Chichocki, A. (2016). Discriminative feature extraction via multivariate linear regression for SSVEP- based BCI. IEEE transactions on neural systems and rehabilitation engineering., 24 , 1-10.World Health Organization. (2011). World report on disability . Geneve. (ISBN 9789241564182)Xiao, X., Xu, M., Jin, J., Wang, Y., Jung, T., y Ming, D. (2019). Discriminative canonical pattern matching for single-trial classification of ERP components. IEEE Transaction on BiomedicalEngineering , 1-1.Xiao, X., Xu, M., Wang, Y., Jung, T., y Ming, D. (2019). comparison of classification methods for recognizing single-trial P300 in brain-computer interfaces. En 41st an- nual international conference of the ieee engineering in medicine and biology society. Berlin, Germany.Xu, M., Long, C., y He, F. (2016). Incorporation of inter-subject information to improve the accuracy of subject-specified P300 classifiers. International Journal of Neural Systems, 26 , 1-10.Zhang, X., Xu, G., Mou, X., Ravi, A., Li, M., Wang, Y., y Jiang, N. (2019). A convolutional neural network for the detection of asynchronous steady state motion visual evoked po- tential. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27 (6), 1303-1311.instname:Universidad Antonio Nariñoreponame:Repositorio Institucional UANrepourl:https://repositorio.uan.edu.co/In recent years, the Brain Computer Interfaces(BCI) have been highly studied, due to they allow to interact with the environment without the requirement to use the peripherical nervious system. Consequently, The appliaction of this, has been very useful in the rehabilitation engineering. However, the traslation of the user's intent through of Electroencephalography(EEG) is still a challenge for the scientific community, consequently, the stimulation that allow to evoke responses in patterns form, for that the system can recognizes them, is necessary. An experiment highly used corresponding to the Oddball paradigm, that through of visual stimulus, allow to evoke a positive deflection in the parieto-central cortex to the 300 ms, when the subject is interested in a specific stimuli between aleatory stimulation, known as P300 potential. The P300 have a problematic in his recognition that consist in a low signal to noise ratio, this generate that the extraction techniques be reason of interest. In the present work, a comparative study between five P300-recognition methods is preformed: two standard methods reported in literature: Mean-Amplitude-LDA(MA-LDA) and Stepwise-LDA(SWLDA), and three novel methods based in the Canonical Correlation Analysis(CCA): MA+CCA-LDA, CCA with Regularizad Logistic Regression and CCA with Multilayer Perceptron(MLP). The methods were validated in a available dataset, that consisted in a BCI-P300 system implemented in a Speller. Using as evaluation metrics: the classification percentage and the computational cost. Also a measurement protocol in healthy people was developed, to implement the BCI-P300 Speller in real time, at the simulation Lab of the Universidad Antonio Nariño, using the device of EEG acquisition g.Nautilus-32 PRO and the public software BCI 2000En los últimos años, las Interfaces Cerebro-Computador(BCI) han sido altamente estudiadas, debido a que permiten interactuar con el entorno sin necesidad de usar el sistema nervioso periférico. Por lo que, su aplicación en el campo de la ingeniería de rehabilitación, ha sido muy útil. Sin embargo, la traducción de la intención del usuario a través de Electroencefalografía todavía sigue siendo un reto para la comunidad científica, por lo que es necesario la estimulación que permita evocar respuestas en patrones que el sistema pueda reconocer. Un experimento altamente usado corresponde al paradigma Oddball, que a través de estimulación visual, permite evocar una deflexión positiva en la corteza parieto-central a los 300 ms cuando al sujeto de pruebas le llama la atención un estímulo específico entre una estimulación aleatoria, conocido como potencial P300. El P300 tiene una problemática en su reconocimiento que consiste en la baja relación señal a ruido, por lo que las técnicas de extracción de esta señal son motivo de interés. En el presente trabajo, se realiza un estudio comparativo entre cinco métodos de reconocimiento P300. dos métodos estándar reportados en la literatura: Mean-Amplitude-LDA(MA-LDA) y Stepwise-LDA(SWLDA), y tres nuevos basados en el análisis de correlación canónica(CCA): MA+CCA-LDA ,CCA con Regresión Logística Regularizada y CCA con Perceptrón Multicapa (CCA-MLP). Los métodos se validaron en una base de datos disponible, que consistió en un sistema BCI-P300 implementado en un deletreador o Speller. Usando como métricas de evaluación:el porcentaje de clasificación y el costo computacional. También se elaboró un protocolo de medición en personas sanas para implementar el sistema BCI-P300 Speller en tiempo real, en el laboratorio de simulación de la Universidad Antonio Nariño, utilizando el dispositivo de adquisición de EEG g.Nautilus-32 PRO y el software público BCI2000.OtroIngeniero(a) Biomédico(a)PregradoValor Total proyecto $28.290.000. Financiación UAN $26.390.000, Financiación propia $1.900.000.PresencialspaUniversidad Antonio NariñoIngeniería BiomédicaFacultad de Ingeniería Mecánica, Electrónica y BiomédicaBogotá - SurInterfaz Cerebro-ComputadorElectroencefalografíaPotencial Relacionado a EventosP300Análisis de Correlación CanónicaBrain-Computer InterfaceElectroencephalographyEvent-Related PotentialP300Canonical Correlation AnalysisEstudio comparativo de métodos para el reconocimiento de potenciales relacionados a eventos P300 para una interfaz cerebro-computadorTrabajo de grado (Pregrado y/o Especialización)http://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/version/c_970fb48d4fbd8a85ORIGINAL2020CristianFelipeBlancoDiaz.pdf2020CristianFelipeBlancoDiaz.pdfTrabajo de Gradoapplication/pdf5409927https://repositorio.uan.edu.co/bitstreams/a2b7bf66-5eb2-4a68-b4e2-e1fbd1145e28/download83d90f801014d8699a9846074e7271e6MD512020AutorizaciondeAutores.pdf2020AutorizaciondeAutores.pdfAutorización de Autoresapplication/pdf599247https://repositorio.uan.edu.co/bitstreams/dc912bb1-8997-442d-b610-860833f8fc1a/downloadf13511c07dca4ee326a20ccb3e373ce6MD52CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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