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Peter J. Ifft

Researcher at Duke University

Publications -  13
Citations -  1245

Peter J. Ifft is an academic researcher from Duke University. The author has contributed to research in topics: Motor control & Brain–computer interface. The author has an hindex of 8, co-authored 13 publications receiving 1084 citations. Previous affiliations of Peter J. Ifft include Edwards Lifesciences Corporation.

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Active tactile exploration using a brain–machine–brain interface

TL;DR: 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 of the primary somatosensory cortex is reported.
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Chronic, wireless recordings of large-scale brain activity in freely moving rhesus monkeys

TL;DR: It is proposed that wireless large-scale recordings could have a profound impact on basic primate neurophysiology research while providing a framework for the development and testing of clinically relevant neuroprostheses.
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A Brain-Machine Interface Enables Bimanual Arm Movements in Monkeys

TL;DR: A bimanual BMI that enables rhesus monkeys to control two avatar arms simultaneously and widespread plasticity in frontal and parietal cortical areas is observed, suggesting that cortical networks may assimilate the two avatar Arms through BMI control.
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Future developments in brain-machine interface research

TL;DR: The future steps that are part of the strategic plan of the Duke University Center for neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the École Polytechnique Fédérale de Lausanne (EPFL) Center for Neuroprosthetics, to bring this new technology to clinical fruition are reviewed.
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Computing Arm Movements with a Monkey Brainet

TL;DR: A Brainet that utilizes very-large-scale brain activity from two (B2) or three (B3) nonhuman primates to engage in a common motor behaviour is introduced, suggesting that primate brains can be integrated into a Brainet, which self-adapts to achieve acommon motor goal.