2013, Número 2
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Investigación en Discapacidad 2013; 2 (2)
Los sistemas de interfaz cerebro-computadora: una herramienta para apoyar la rehabilitación de pacientes con discapacidad motora
Gutiérrez-Martínez J, Cantillo-Negrete J, Cariño-Escobar RI, Elías-Viñas D
Idioma: Español
Referencias bibliográficas: 36
Paginas: 62-69
Archivo PDF: 269.63 Kb.
RESUMEN
En los últimos 15 años se han desarrollado en diversas partes del mundo nuevos sistemas para la rehabilitación de pacientes con desórdenes neuromusculares severos, como esclerosis lateral amiotrófica, infarto cerebral y lesión medular. Estos sistemas han sido denominados como interfaces cerebro-computadora. Los sistemas interfaces cerebro-computadora buscan proveer a sus usuarios de capacidades de comunicación básicas, como operar programas de selección de
palabras en una computadora o controlar una neuroprótesis. Los sistemas interfaces cerebrocomputadora descifran la intención del usuario de moverse o comunicarse a través del análisis de señales eléctricas cerebrales que incluyen potenciales corticales lentos, potenciales evocados visuales, potencial P300, y ritmos beta o mu registrados sobre el cuero cabelludo, así como la actividad neuronal cortical registrada mediante electrodos implantados. Estas señales son convertidas en comandos para operar una computadora o algún otro dispositivo por medio de un procesamiento digital efectuado en tiempo real. En este artículo se muestra un panorama general de los sistemas interfaces cerebro-computadora, sus características y aplicaciones con mayor relevancia orientadas a mejorar la calidad de vida de los pacientes con discapacidad motora.
REFERENCIAS (EN ESTE ARTÍCULO)
Mason SG, Birch GE. A general framework for braincomputer interface design. IEEE Trans Neural Syst and Rehab Eng. 2006; 11: 70-85.
Schalk G, McFarland DJ, Hinterberger T, Birbaumer N, Wolpaw JR. BCI2000: a general-purpose braincomputer interface (BCI) system. IEEE Trans Biomed Eng. 2004; 51: 1034-1043.
Rokni U, Richardson AG, Bizzi E, Seung HS. Motor learning with unstable neural representations. Neuron. 2007; 54: 653-666.
Leuthardt EC, Schalk G, Wolpaw JR, Ojemann JG, Moran DW. A brain-computer interface using electrocortic graphic signals in humans. J Neural Eng. 2004; 1: 63-71.
Kennedy, PR, Bakay RAE, Moore MM, Adams KD, Goldwaithe J. Direct control of a computer from the human central nervous system. IEEE Trans Rehabil Eng. 2000; 8: 198-202.
Hochberg L, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH et al. Neuronal ensemble control of prosthetic devices by a human with tretraplegia. Nature. 2006; 442: 164-171.
Taylor D, Tillery SI, Schwartz AB. Direct cortical control of 3D neuroprosthetic devices. Science. 2002; 296: 1829-1832.
Musallam S, Greger B, Scherberger H, Andersen RA. Cognitive control signals for neural prosthetics. Science. 2004; 305: 258-262.
Carmena JM, Lebedev MA, Crist RE, O’Doherty JE, Santucci DM, Dimitrov DF et al. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol. 2003; 1: 42.
Wang R, Gao X, Hong B, Gao S. A practical VEP–based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng. 2006; 14: 234-240.
Friman O, Volosyak I, Gräser A. Multiple channel detection of steady–state visual evoked potentials for brain-computer interfaces. IEEE Trans Biomed Eng. 2007; 54: 742-750.
Sutton S, Braren M, Zubin J, John ER. Evoked-potentials correlates of stimulus uncertainty. Science. 1965; 150: 1187-1188.
Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephal Clin Neurophys. 1988; 70: 510-523.
Pfurtscheller G, Neuper C. Motor imagery activates primary sensorimotor area in humans. Neurosci Lett.1997; 239: 65-68.
Neuper C, Müller GR, Kübler A, Birbaumer N, Pfurt cheller G. Clinical application of an EEG-based brain–computer interface: a case study in a patient with severe motor impairment. J Clin Neurophysiol. 2003; 114: 399-409.
Wolpaw J, McFarland DJ. Control of two-dimensional movement signal by a noninvasive brain-computer interface in humans. PNAS. 2004; 101: 17849-17854.
Wolpaw J, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM. Brain–computer interfaces for communication and control. J Clin Neurophysiol. 2002; 113: 767-791.
Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B, Kübler A, Perelmouter J et al. A spelling device for the paralysed. Nature. 1999; 398: 297-298.
Hoffmann U, Vesin JM, Ebrahimi, T. Recent advances in brain-computer interfaces (Invited Paper). 2007 MMSP; 1: 7-17.
Schlögl A, Lee F, Bischof H, Pfurtscheller G. Characterization of four-class motor imagery EEG data for the BCI-competition 2005. J Neural Eng. 2005; 1: 14-22.
Lalor E, Kelly SP, Finucane C, Burke R, Smith R, Reilly RB. Steady-state VEP-based brain-computer interface control in an immersive 3D gaming environment. EURASIP J Appl Sig Proc. 2005, 3156-3164.
Lemm S, Schafer C, Curio G. Probabilistic modeling of sensorimotor mu-rhythms for classification of imaginary hand movements. IEEE Trans Biomed Eng. 2003; 51: 1077-1080.
Gysels E, Celka P. Phase synchronization for the recognition of mental tasks in a brain-computer interface. IEEE Trans Neural Syst and Rehabil Eng. 2003; 12: 406-415.
McFarland D, McCane LM, David SV, Wolpaw JR. Spatial filter selection for EEG-based communication. Electroenceph clin Neurophysiol. 1997; 103: 386-394.
Ramoser H, Muller-Gerking J, Pfurtscheller G. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng. 2000; 8: 441-446.
Makeig S, Bell AJ, Jung TP, Sejnowski TJ. Independent component analysis of electroencephalographic data. Adv NIPS. 1997; 145-151.
Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. Data mining, inference, and prediction. 2nd edition Stanford CA: Springer; 2001.
Bashashati A, Fatourechi M, Ward RK, Birch GE. A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. J Neural Eng. 2007; 2: 32-57.
Thulasidas M, Guan C, Wu J. Robust classification of EEG signal for brain-computer interface. IEEE Trans Neural Syst Rehabil Eng. 2006; 14: 24-29.
Gao X Xu D, Cheng M, Gao SK. A BCI-based environmental controller for the motion-disabled. IEEE Trans Neural Syst Rehabil Eng. 2003; 11: 137-140.
Leeb R, Friedman D, Müller-Putz GR, Scherer R, Slater M, Pfurtscheller G. Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic. J Comput Intell Neurosci. 2007; 2007: 79642.
Galán F, Nuttin M, Lew E, Ferrez PW, Vanacker G, Philips J et al. A brain-actuated wheelchair: asynchronous and non-invasive brain-computer interfaces for continuous control of robots. J Clin Neurophysiol. 2008; 119: 2159-2169.
Taylor DM, Tillery SI, Schwartz AB. Information conveyed through brain-control: cursor versus robot. IEEE Trans Neural Syst Rehabil Eng. 2003; 11: 195-199.
Pfurtscheller G, Müller-Putz GR, Pfurtscheller J, Rupp R. EEG-Based asynchronous BCI controls functional electrical stimulation in a tetraplegic patient. EURASIP J Appl Sig Proc. 2005: 3152-3155.
Birbaumer N, Weber C, Neuper C, Buch E, Haapen K, Cohen L. Physiological regulation of thinking: braincomputer interface (BCI) research. Prog Brain Res. 2006; 159: 369-391.
g.Tec medical engineering. (2011). IntendiX: User- Ready Brain-Computer Interface Applications. Fecha de Consulta: 1 de agosto de 2012. Disponible en: www. intendix.com