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Active tactile exploration using a brain–machine–brain interface


Brain–machine interfaces1,2 use neuronal activity recorded from the brain to establish direct communication with external actuators, such as prosthetic arms. It is hoped that brain–machine interfaces can be used to restore the normal sensorimotor functions of the limbs, but so far they have lacked tactile sensation. Here we report the operation of a brain–machine–brain interface (BMBI) that both controls the exploratory reaching movements of an actuator and allows signalling of artificial tactile feedback through intracortical microstimulation (ICMS) of the primary somatosensory cortex. Monkeys performed an active exploration task in which an actuator (a computer cursor or a virtual-reality arm) was moved using a BMBI that derived motor commands from neuronal ensemble activity recorded in the primary motor cortex. 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 for and distinguish one of three visually identical objects, using the virtual-reality arm to identify the unique artificial texture 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.

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Figure 1: The brain–machine–brain interface.
Figure 2: Learning to use ICMS feedback.
Figure 3: Statistics of object exploration.
Figure 4: M1 modulations during active control versus passive observation.

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We thank D. Dimitrov for conducting the animal neurosurgeries; G. Lehew and J. Meloy for building brain implants; J. Fruh for rendering the virtual-reality monkey arm; T. Phillips, L. Oliveira and S. Halkiotis for technical support; and E. Thomson and Z. Li for comments. This research was supported by DARPA (award N66001-06-C-2019), TATRC (award W81XWH-08-2-0119), the NIH (award NS073125), NICHD/OD (award RC1HD063390) and NIH Director’s Pioneer Award DP1OD006798, to M.A.L.N. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Office of the NIH Director or the NIH.

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Authors and Affiliations



J.E.O’D., M.A.L. and M.A.L.N. designed experiments, analysed data and wrote the paper; J.E.O’D., M.A.L., P.J.I. and K.Z.Z. conducted experiments; and S.S. and H.B. developed the virtual-reality monkey arm.

Corresponding author

Correspondence to Miguel A. L. Nicolelis.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Figures

This file contains Supplementary Figures 1-7 with legends. (PDF 3026 kb)

Supplementary Movie 1

This movie sequence depicts monkey M performing task IV in HC mode. Annotations are provided during the freeze frames. The audio track is the spiking activity of a single M1 neuron; ICMS pulses can be completion of a trial and the delivery of a juice reward. Please note that the monkeys could not hear the ICMS pulses or neural activity; these sounds are included for the benefit of the viewer. (MOV 11640 kb)

Supplementary Movie 2

This movie depicts the same sequence as Supplementary Movie 1 but for BC. Please note that the monkeys could not hear the ICMS pulses or neural activity; these sounds are included for the benefit of the viewer. (MOV 14860 kb)

Supplementary Movie 3

This movie sequence depicts monkey M performing task V in HC. Please note that the monkeys could not hear the ICMS pulses or neural activity; these sounds are included for the benefit of the viewer. (MOV 14763 kb)

Supplementary Movie 4

This movie depicts the same sequence as Supplementary Movie 3 but for BC. Please note that the monkeys could not hear the ICMS pulses or neural activity; these sounds are included for the benefit of the viewer. (MOV 16711 kb)

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O’Doherty, J., Lebedev, M., Ifft, P. et al. Active tactile exploration using a brain–machine–brain interface. Nature 479, 228–231 (2011).

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