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Active tactile exploration enabled by a brain-machine-brain interface

Brain-machine interfaces (BMIs)(1,2) use neuronal activity recorded from the brain to establish direct communication with external actuators, such as prosthetic arms. While BMIs aim to restore the normal sensorimotor functions of the limbs, so far they have lacked tactile sensation. Here we demonstr...

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Detalles Bibliográficos
Autores principales: O’Doherty, Joseph E., Lebedev, Mikhail A., Ifft, Peter J., Zhuang, Katie Z., Shokur, Solaiman, Bleuler, Hannes, Nicolelis, Miguel A. L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3236080/
https://www.ncbi.nlm.nih.gov/pubmed/21976021
http://dx.doi.org/10.1038/nature10489
Descripción
Sumario:Brain-machine interfaces (BMIs)(1,2) use neuronal activity recorded from the brain to establish direct communication with external actuators, such as prosthetic arms. While BMIs aim to restore the normal sensorimotor functions of the limbs, so far they have lacked tactile sensation. Here we demonstrate the operation of a brain-machine-brain interface (BMBI) that both controls the exploratory reaching movements of an actuator and enables the signalling of artificial tactile feedback through intracortical microstimulation (ICMS) of the primary somatosensory cortex (S1). Monkeys performed an active-exploration task in which an actuator (a computer cursor or a virtual-reality hand) was moved using a BMBI that derived motor commands from neuronal ensemble activity recorded in primary motor cortex (M1). ICMS feedback occurred whenever the actuator touched virtual objects. Temporal patterns of ICMS encoded the artificial tactile properties of each object. Neuronal recordings and ICMS epochs were temporally multiplexed to avoid interference. Two monkeys operated this BMBI to search and discriminate one out of three visually undistinguishable objects, using the virtual hand to identify the unique artificial texture (AT) associated with each. These results suggest that clinical motor neuroprostheses might benefit from the addition of ICMS feedback to generate artificial somatic perceptions associated with mechanical, robotic, or even virtual prostheses.