El presente artículo describe una interfaz cerebro-computador (BCI: Brain-Computer Interface) que permite gobernar un brazo robótico. El sistema emplea señales electroencefalográficas (EEG) captadas por 16 electrodos para controlar el robot mediante potenciales evocados visuales, concretamente a través del paradigma P300 y N2PC. De esta manera, usando estímulos visuales, el usuario es capaz de controlar el movimiento del robot, centrando su atención en las diferentes opciones que se le muestran en una pantalla. El sistema ha sido validado de forma satisfactoria por tres usuarios sanos, cada uno de los cuales realizó diversas tareas de agarre y colocación de objetos controlando un brazo robot de 6 grados de libertad.
Información de la revista
Vol. 8. Núm. 2.
Páginas 103-111 (abril 2011)
Vol. 8. Núm. 2.
Páginas 103-111 (abril 2011)
Open Access
Interfaz Cerebral no Invasiva basada en Potenciales Evocados para el Control de un Brazo Robot
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4108
José L. Sirvent, José M. Azorín, Eduardo Iáñez, Andrés Úbeda, Eduardo Fernández
Grupo de Neuroingeniería Biomédica, Universidad Miguel Hernández de Elche, Avda. de la Universidad s/n, 03202, Elche (Alicante), España
Este artículo ha recibido
Información del artículo
Resumen
Palabras clave:
Interfaz cerebro-computador
interfaz hombre-robot
brazo robot
control
potenciales evocados
El Texto completo está disponible en PDF
Referencias
[American Electroencephalographic Society, 1991]
American Electroencephalographic Society.
American Electroencephalography Society guidelines for standard electrode position nomenclature.
Journal of Clinical Neurophysiology, 8 (1991), pp. 200-202
[Bensch et al., 2007]
M. Bensch, A.A. Karim, J. Mellinger, T. Hinterberger, M. Tangermann, W. Rosenstiel, N. Birdbaumer.
Nessi: An EEG-controlled web browser for severely paralyzed patients.
Computational Intelligence and Neuroscience, 2007, Article ID 71863, (2007), pp. 5
[Carmena et al., 2003]
J.M. Carmena, M.A. Lebedev, R.E. Crist, J.E. O’Doherty, D.M. Santucci, D.F. Dimitrov, P.G. Patil, C.S. Henriquez, M.A.L. Nicolelis.
Learning to control a brain-machine interface for reaching and grasping by primates.
PloS Biol, 1 (2003), pp. 193-208
[Covingto et al., 1996]
James W. Covingto, J. Polich.
P300, stimulus intensity, and modalit.
Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section, 100 (1996), pp. 579-584
[Danoczy et al., 2008a]
M. Danoczy, S. Fazli, C. Grozea, K.R. Müller, F. Popescu.
Brain2robot: a grasping robot arm controlled by gaze and asynchronous EEG BCI.
Proceedings of the 4th International Brain–Computer Interface Workshop and Training Course, pp. 355-360
[Danoczy et al., 2008b]
M. Danoczy, S. Fazli, C. Grozea, K.R. Müller, F. Popescu.
Brain2robot: a grasping robot arm controlled by gaze and asynchronous EEG BCI.
Proceedings of the 4th International Brain–Computer Interface Workshop and Training Course, pp. 355-360
[Martin., 1996]
E. Martin.
The N2pc component as an indicator of attentional selectivity.
Electroencephalography and clinical Neurophysiology 1990, 99 (1996), pp. 225-234
[Farwell and Donchin, 1998]
L.A. Farwell, E. Donchin.
Talking off the top of your head; Torward a mental Prosthesis utilizing event-related brain potentials.
Electroenceph. Clin. Neurophysiol., 70 (1998), pp. 510-523
[Handy, 2005]
T.C. Handy.
Event-related potentials, a methods handbook., (2005),
[Iáñez et al., 2010]
E. Iáñez, J.M. Azorín, A. Úbeda, J.M. Ferrández, E. Fernández.
Mental tasks-based brain-robot interface.
Robotics and Autonomous Systems, 58 (2010), pp. 1238-1245
[Iturrate et al., 2009]
I. Iturrate, J. Antelis, A. Kubler, J. Minguez.
A noninvasive brain-actuated weelchair based on a P300 neurophysiological protocol and automated navigation.
IEEE Transactions on Robotics, 25 (2009), pp. 614-627
[Johnson and Krusienski, 2009]
G.D. Johnson, D.J. Krusienski.
Ensemble SWLDA classifiers for the P300 speller.
Human-Computer Interaction. Novel Interaction Methods and Techniques, 5611 (2009), pp. 551-557
[Kiss et al., 2008]
M. Kiss, J. Van Velzen, M. Eimer.
The N2pc component and its links to attention shift and spatially selective visual processing.
Psychophysiology, 45 (2008), pp. 240-249
[Krusienski et al., 2006]
D.J. Krusienski, E.W. Sellers, F. Cabestaing, S. Bayoudh, D.J. McFarland, T.M. Vaughan, J.R. Wolpaw.
A comparison of classification techniques for the P300 Speller.
Journal of Neural Engineering, 3 (2006), pp. 299-305
[Luck et al., 1990]
S.J. Luck, H.J. Heinze, G.R. Mangun, S.A. Hillyard.
Visual event-related potentials index focused attention within bilateral stimulus arrays. Functional dissociation of P1 and N1 components.
Electroencephalography and clinical Neurophysiology, 75 (1990),
[Mirghasemi and Fazel-Rezai, 2006]
H. Mirghasemi, R. Fazel-Rezai.
Analysis of P300 Classifiers in brain computer interface speller.
Engineering in Medicine and Biology Society, pp. 6205-6208
[Millán et al., 2002]
J. Millán, R. del, M. Franzé, J. Mouriño, F. Cincotti, F. Babiloni.
Relevant EEG features for the classification of spontaneous motor-related tasks.
Biological Cybernetics, 86 (2002), pp. 89-95
[Mügler et al., 2008]
E. Mügler, M. Bensch, S. Halder, W. Rosenstiel, M. Bogdan, N. Birbaumer, A. Kubler.
Control of an Internet browser using the P300 event related potential.
International Journal of Bioelectromagnetism., 10 (2008), pp. 56-63
[Müller and Blankertz, 2006]
K.R. Müller, B. Blankertz.
Toward noninvasive brain computer interfaces.
IEEE Signal Process Mag, 23 (2006), pp. 125-128
[Obermaier et al., 2003]
B. Obermaier, G.R. Muller, G. Pfurtscheller.
Virtual keyboard controlled by spontaneous EEG activity.
IEEE Trans. Neural Sys. Rehab. Eng., 11 (2003), pp. 422-426
[Palankar et al., 2008]
M. Palankar, K.J. De Laurentis, R. Alqasemi, E. Veras, R. Dubey, Y. Arbel, E. Donchin.
Control of a 9-DoF wheelchair-mounted robotic arm system using a P300 brain computer interface: initial experiments.
Robotics and Biomimetics, pp. 348-353
[Purves et al., 2006]
D. Purves, G. Augustine, D. Fitzpatrick, W. Hall, A.S. LaMantia, J. McNamara, S. Williams.
Neurociencia.
3a Edición, Editorial Medica Panamericana, (2006),
[Schalk et al., 2004]
G. Schalk, D.J. McFarland, T. Hinterberger, N. Birbaumer, J.R. Wolpaw.
BCI2000: A general-purpose braincomputer interface (BCI) system.
IEEE Transactions on Biomedical Engineering, 51 (2004), pp. 1034-1040
[Sirvent et al., 2010]
J.L. Sirvent, J.M. Azorín, E. Iáñez, A. Úbeda, E. Fernández.
P300-based BCI for internet browsing.
pp. 615-622
[Touyama and Hirose, 2008]
H. Touyama, M. Hirose.
Non-target photo images in oddball paradigm improve EEG-based personal identification rates.
Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, pp. 4118-4121
[Velliste et al., 2008]
M. Velliste, S. Perel, M.C. Spalding, A.S. Whitford, A.B. Schwartz.
Cortical control of a prosthetic arm for self-feeding.
Nature, 453 (2008), pp. 1098-1101
[Wang et al., 2008]
Y. Wang, X. Gao, B. Hong, C. Jia, S. Gao.
Brain-computer interfaces based on visual evoked potentials.
IEEE Engineering in medicine and biology magazine, 27 (2008), pp. 64-71
[Wolpaw et al., 2008]
J.R. Wolpaw, D.J. McFarland, G.W. Neat, C.A. Porneris.
An EEG-based brain-computer interface for cursor control.
Electroencephalographic Clinical Neurophysiology, 78 (2008), pp. 252-259
[Wolpaw et al., 2002]
J.R. Wolpaw, N. Birbaumer, D.J. McFarland, G. Pfurtscheller, T.M. Vaughan.
Brain-computer interfaces for communication and control.
Clinical Neurophysiology, 113 (2002), pp. 767-791
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