Temporal coding of brain patterns for direct limb control in humans

For individuals with a high spinal cord injury (SCI) not only the lower limbs, but also the upper extremities are paralyzed. A neuroprosthesis can be used to restore the lost hand and arm function in those tetraplegics. The main problem for this group of individuals, however, is the reduced ability...

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Vydáno v:Frontiers in neuroscience Ročník 4
Hlavní autoři: Müller-Putz, Gernot R, Scherer, Reinhold, Pfurtscheller, Gert, Neuper, Christa
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland Frontiers Research Foundation 01.01.2010
Frontiers Media S.A
Témata:
Activity patterns
Amyotrophic lateral sclerosis
Arm
Brain
brain-computer interface (BCI)
Communication
Computer applications
EEG
Elbow
electroencephalogram (EEG)
Emulation
Implants
Information processing
Mental task performance
Motor Imagery
Neural coding
Neuroprosthesis
Neuroscience
Prosthetics
Spinal cord injuries
spinal cord injury (SCI)
Temporal variations
Germany
spinal cord injury
brain–computer interface
motor imagery
neuroprosthesis
electroencephalogram
ISSN:1662-453X, 1662-4548, 1662-453X
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Shrnutí:For individuals with a high spinal cord injury (SCI) not only the lower limbs, but also the upper extremities are paralyzed. A neuroprosthesis can be used to restore the lost hand and arm function in those tetraplegics. The main problem for this group of individuals, however, is the reduced ability to voluntarily operate device controllers. A brain-computer interface provides a non-manual alternative to conventional input devices by translating brain activity patterns into control commands. We show that the temporal coding of individual mental imagery pattern can be used to control two independent degrees of freedom - grasp and elbow function - of an artificial robotic arm by utilizing a minimum number of EEG scalp electrodes. We describe the procedure from the initial screening to the final application. From eight naïve subjects participating online feedback experiments, four were able to voluntarily control an artificial arm by inducing one motor imagery pattern derived from one EEG derivation only.
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Edited by: José del R. Millán, Ecole Polytechnique Fédérale de Lausanne, Switzerland
Reviewed by: Silvestro Micera, Scuola Superiore Sant'Anna, Italy; Kim Dremstrup, Aalborg University, Denmark
ISSN:1662-453X
1662-4548
1662-453X
DOI:10.3389/fnins.2010.00034