Brain-computer interface systems: a tool to support the rehabilitation of patients with motor disabilities
Keywords:
Brain-computer interfaces, rehabilitation, electroencephalography, neuromotor diseaseAbstract
On the last 15 years a new field of technological research has arisen in order to develop rehabili-
tation 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 inter-
faces 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.
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References
Mason SG, Birch GE. A general framework for brain-
computer 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;
: 1034-1043.
Rokni U, Richardson AG, Bizzi E, Seung HS. Motor
learning with unstable neural representations. Neuron.
; 54: 653-666.
Leuthardt EC, Schalk G, Wolpaw JR, Ojemann JG,
Moran DW. A brain-computer interface using electro-
cortic graphic signals in humans. J Neural Eng. 2004;
: 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, Sa-
leh M, Caplan AH et al. Neuronal ensemble control of
prosthetic devices by a human with tretraplegia. Nature.
; 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.
; 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–ba-
sed 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.
; 54: 742-750.
Sutton S, Braren M, Zubin J, John ER. Evoked-potentials
correlates of stimulus uncertainty. Science. 1965; 150:
-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:
-523.
Pfurtscheller G, Neuper C. Motor imagery activates pri-
mary sensorimotor area in humans. Neurosci Lett.1997;
: 65-68.
Neuper C, Müller GR, Kübler A, Birbaumer N, Pfurt
cheller G. Clinical application of an EEG-based bra-
in–computer interface: a case study in a patient with
severe motor impairment. J Clin Neurophysiol. 2003;
: 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 communica-
tion 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;
: 7-17.
Schlögl A, Lee F, Bischof H, Pfurtscheller G. Charac-
terization 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
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