Cortical Response to Expectation of Tactile Stimulation from External Anthropomorphic and Non-Anthropomorphic Systems

Luke E. Osborn*, Breanne Christie, Adam C.G. Crego, Dayann D'Almeida, David P. McMullen, Robert W. Nickl, Ambarish S. Pawar, Jeremy D. Brown, Brock A. Wester, Chaz Firestone, Pablo A. Celnik, Matthew S. Fifer, Francesco V. Tenore

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Complete sensorimotor integration and user acceptance of a neural prosthesis relies on system embodiment - the incorporation of an external system into one's own body schema and representation. Embodiment of neural prostheses is an ambiguous concept with limited approaches for quantifying human and machine integration in a meaningful way. In an attempt to understand human sensory integration with external systems, we measured neural activity in the somatosensory cortex of a participant with chronically implanted microelectrode arrays during sensory events tied to either a virtual robotic hand touching an object or a virtual lamp lighting up. Sensory stimulation was delivered using either skin vibration or intracortical microstimulation (ICMS) and was mapped to the virtual systems. Through the brain-machine interface, we observed quantifiable cortical activity corresponding to tactile sensations perceived during the virtual tasks and even during instances when neural stimulation was expected but not delivered, demonstrating the presence of sensory-related neural activity even in the absence of tactile stimulation. Evoked sensory expectation signals were also observed in the motor cortex, although at reduced amplitudes. Evoked cortical activity corresponding to expectation of a sensory input could serve as objective cortical markers for better understanding sensori-motor integration and perceptual experiences when connecting humans with external systems.

Original languageEnglish (US)
Title of host publication11th International IEEE/EMBS Conference on Neural Engineering, NER 2023 - Proceedings
PublisherIEEE Computer Society
ISBN (Electronic)9781665462921
DOIs
StatePublished - 2023
Event11th International IEEE/EMBS Conference on Neural Engineering, NER 2023 - Baltimore, United States
Duration: Apr 25 2023Apr 27 2023

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2023-April
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

Conference11th International IEEE/EMBS Conference on Neural Engineering, NER 2023
Country/TerritoryUnited States
CityBaltimore
Period4/25/234/27/23

Funding

The authors thank the participant for their time spent on this study. This research was developed with funding from the Johns Hopkins University Applied Physics Laboratory (JHU/APL). Experimental infrastructure was enabled by previous funding from the Defense Advanced Research Projects Agency (DARPA). The views, opinions and/or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. 1Johns Hopkins University Applied Physics Laboratory 2National Institute of Mental Health, National Institutes of Health 3Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine 4Department of Mechanical Engineering, Johns Hopkins University 5Department of Cognitive Science, Johns Hopkins University correspondance: [email protected]

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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