Toward a high-performance, robust brain-machine interface

Abstract

Event Abstract Back to Event Toward a high-performance, robust brain-machine interface Krishna V. Shenoy1* 1 Stanford University, Depts. of Electrical Engineering, Bioengineering, and Neurobiology, United States Brain-machine interfaces (BMIs) translate neural activity from the brain into control signals for guiding prosthetic devices, such as computer cursors and robotic limbs, offering disabled patients greater interaction with the world. BMIs have recently demonstrated considerable promise in proof-of-concept animal experiments and in human clinical trials. However, at least two critical barriers to successful translation remain. First, current BMIs are considerably slower and have less accurate control than the native arm. Second, current BMIs do not sustain performance across hours and days, or across behavioral tasks without human intervention. To address this need for increased performance and robustness, we recently conducted BMI experiments with rhesus monkeys implanted with electrode arrays in motor cortices. Initial experiments informed the design and training of a control algorithm, the recalibrated feedback intention-trained Kalman filter (ReFIT-KF) that mathematically incorporates the feedback-control nature of BMIs. Subsequent experiments demonstrated that the ReFIT-KF algorithm approximately doubles performance relative to state-of-the-art existing BMIs, and approaches native arm performance for cursor control tasks. This BMI also demonstrated robustness across years and behavioral-contexts. Taken together, this new level of performance and robustness should help increase the clinical viability of BMIs. Keywords: Motor Decoding and Brain-Machine Interfaces Conference: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011. Presentation Type: Keynote Topic: other Citation: Shenoy KV (2011). Toward a high-performance, robust brain-machine interface. Front. Comput. Neurosci. Conference Abstract: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011. doi: 10.3389/conf.fncom.2011.53.00005 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 26 Sep 2011; Published Online: 04 Oct 2011. * Correspondence: Prof. Krishna V Shenoy, Stanford University, Depts. of Electrical Engineering, Bioengineering, and Neurobiology, Stanford, United States, shenoy@stanford.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Krishna V Shenoy Google Krishna V Shenoy Google Scholar Krishna V Shenoy PubMed Krishna V Shenoy Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.