Sense of agency for intracortical brain–machine interfaces

Andrea Serino (), Marcia Bockbrader, Tommaso Bertoni, Sam Colachis Iv, Marco Solcà, Collin Dunlap, Kaitie Eipel, Patrick Ganzer, Nick Annetta, Gaurav Sharma, Pavo Orepic, David Friedenberg, Per Sederberg, Nathan Faivre, Ali Rezai and Olaf Blanke ()
Additional contact information
Andrea Serino: University Hospital Lausanne (CHUV)
Marcia Bockbrader: The Ohio State University
Tommaso Bertoni: University Hospital Lausanne (CHUV)
Sam Colachis Iv: The Ohio State University
Marco Solcà: Campus Biotech
Collin Dunlap: The Ohio State University
Kaitie Eipel: The Ohio State University
Patrick Ganzer: Battelle Memorial Institute
Nick Annetta: Battelle Memorial Institute
Gaurav Sharma: Battelle Memorial Institute
Pavo Orepic: Campus Biotech
David Friedenberg: Battelle Memorial Institute
Per Sederberg: University of Virginia
Nathan Faivre: Campus Biotech
Ali Rezai: The Ohio State University
Olaf Blanke: Campus Biotech

Nature Human Behaviour, 2022, vol. 6, issue 4, 565-578

Abstract: Abstract Intracortical brain–machine interfaces decode motor commands from neural signals and translate them into actions, enabling movement for paralysed individuals. The subjective sense of agency associated with actions generated via intracortical brain–machine interfaces, the neural mechanisms involved and its clinical relevance are currently unknown. By experimentally manipulating the coherence between decoded motor commands and sensory feedback in a tetraplegic individual using a brain–machine interface, we provide evidence that primary motor cortex processes sensory feedback, sensorimotor conflicts and subjective states of actions generated via the brain–machine interface. Neural signals processing the sense of agency affected the proficiency of the brain–machine interface, underlining the clinical potential of the present approach. These findings show that primary motor cortex encodes information related to action and sensing, but also sensorimotor and subjective agency signals, which in turn are relevant for clinical applications of brain–machine interfaces.

Date: 2022
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DOI: 10.1038/s41562-021-01233-2

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