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Dynamic Analysis of Naive Adaptive Brain-Machine Interfaces

Kowalski, Kevin C. and He, Bryan D. and Srinivasan, Lakshminarayan (2013) Dynamic Analysis of Naive Adaptive Brain-Machine Interfaces. Neural Computation, 25 (9). pp. 2373-2420. ISSN 0899-7667. doi:10.1162/NECO_a_00484. https://resolver.caltech.edu/CaltechAUTHORS:20131216-133733239

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Abstract

The closed-loop operation of brain-machine interfaces (BMI) provides a context to discover foundational principles behind human-computer interaction, with emerging clinical applications to stroke, neuromuscular diseases, and trauma. In the canonical BMI, a user controls a prosthetic limb through neural signals that are recorded by electrodes and processed by a decoder into limb movements. In laboratory demonstrations with able-bodied test subjects, parameters of the decoder are commonly tuned using training data that include neural signals and corresponding overt arm movements. In the application of BMI to paralysis or amputation, arm movements are not feasible, and imagined movements create weaker, partially unrelated patterns of neural activity. BMI training must begin naive, without access to these prototypical methods for parameter initialization used in most laboratory BMI demonstrations. Naive adaptive BMI refer to a class of methods recently introduced to address this problem. We first identify the basic elements of existing approaches based on adaptive filtering and define a decoder, ReFIT-PPF to represent these existing approaches. We then present Joint RSE, a novel approach that logically extends prior approaches. Using recently developed human- and synthetic-subjects closed-loop BMI simulation platforms, we show that Joint RSE significantly outperforms ReFIT-PPF and nonadaptive (static) decoders. Control experiments demonstrate the critical role of jointly estimating neural parameters and user intent. In addition, we show that nonzero sensorimotor delay in the user significantly degrades ReFIT-PPF but not Joint RSE, owing to differences in the prior on intended velocity. Paradoxically, substantial differences in the nature of sensory feedback between these methods do not contribute to differences in performance between Joint RSE and ReFIT-PPF. Instead, BMI performance improvement is driven by machine learning, which outpaces rates of human learning in the human-subjects simulation platform. In this regime, nuances of error-related feedback to the human user are less relevant to rapid BMI mastery.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1162/NECO_a_00484DOIArticle
http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00484PublisherArticle
Additional Information:© 2013 Massachusetts Institute of Technology. Received October 1, 2012; accepted February 28, 2013; posted Online July 29, 2013. K.K. and L.S. conceived of and designed the research. K.K. and B.H. performed the experiments. K.K., B.H., and L.S. analyzed the results of the experiments and prepared the figures. K.K. and L.S. drafted the manuscript. K.K., B.H., and L.S. edited and revised the manuscript and approved the final version of the manuscript. L.S. is the corresponding author. K.K. was supported by funding from the UCLA Amgen Scholars Program. L.S. was supported by funding from the American Heart Association Scientist Development grant (11SDG7550015), the DARPA Reliable Neural-Interface Technology (RE-NET) Program, and the UCLA Radiology Exploratory Development Grant. We thank Alexander Wein for his help in organizing experiments and Theodore Koenig, Luis Armendariz, and Siamak Yousefi for their help in beta testing the MATLAB-Kinect interface installation procedure described in our online tutorial.We declare no competing financial interests.
Funders:
Funding AgencyGrant Number
UCLA Amgen Scholars ProgramUNSPECIFIED
American Heart Association Scientist Development Grant11SDG7550015
DARPA Reliable Neural-Interface Technology (RE-NET) ProgramUNSPECIFIED
UCLA Radiology Exploratory Development GrantUNSPECIFIED
Issue or Number:9
DOI:10.1162/NECO_a_00484
Record Number:CaltechAUTHORS:20131216-133733239
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20131216-133733239
Official Citation:Kowalski, K. C., B. D. He, et al. (2013). "Dynamic Analysis of Naive Adaptive Brain-Machine Interfaces." Neural Computation 25(9): 2373-2420.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:43026
Collection:CaltechAUTHORS
Deposited By: David McCaslin
Deposited On:17 Dec 2013 21:44
Last Modified:10 Nov 2021 16:31

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