2013, Number 2
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Investigación en Discapacidad 2013; 2 (2)
Brain-computer interface systems: a tool to support the rehabilitation of patients with motor disabilities
Gutiérrez-Martínez J, Cantillo-Negrete J, Cariño-Escobar RI, Elías-Viñas D
Language: Spanish
References: 36
Page: 62-69
PDF size: 269.63 Kb.
ABSTRACT
On the last 15 years a new field of technological research has arisen in order to develop rehabilitation devices, they are called, brain computer interfaces. The main goal of these systems is to improve the quality of life of people with motor disabilities because of neuromuscular disorders
like, amyotrophic lateral sclerosis, brain stroke and spinal cord injury. The brain computer interfaces systems provide these users with communication capabilities, for example, operate software to select letters in a computer or control neuroprostheses. These systems determine the user’s intentions to move or communicate, through the processing of electrical brain signals, typically, slow cortical potentials, visual evoked potentials, P300 potential, beta and mu rhythms, which are recorded on the scalp, and cortical neuronal activity, recorded by implanted electrodes in the brain. The recorded signals are translated into commands to operate in real-time a computer or another device. A successful operation requires that the user codes commands into brain signals and the brain computer interfaces system decodes the signals to identify these commands. This paper presents an introduction to brain computer interfaces research, characteristics, and the main applications to improve the quality life of people with motor disabilities.
REFERENCES
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