Journal
JOURNAL OF NEURAL ENGINEERING
Volume 11, Issue 2, Pages -Publisher
IOP PUBLISHING LTD
DOI: 10.1088/1741-2560/11/2/026001
Keywords
brain-computer interface; classification; stable decoding
Categories
Funding
- National Science Foundation
- NDSEG fellowships
- Burroughs Wellcome Fund
- Christopher Reeve Paralysis Foundation
- Stanford University Graduate Fellowship
- Texas Instruments Stanford Graduate Fellowship
- Stanford NIH Medical Scientist Training Program grant
- Soros Fellowship
- NIH [T90 DA022762, R90 DA02342606, CRCNS R01-NS054283]
- Stanford CIS, Sloan Foundation
- McKnight Endowment Fund for Neuroscience
- NIH Director's Pioneer Award [1DP1OD006409]
- DARPA Revolutionizing Prosthetics program [N66001-06-C-8005]
- DARPA REPAIR [N66001-10-C-2010]
Ask authors/readers for more resources
Objective. Intracortical brain-computer interface (BCI) decoders are typically retrained daily to maintain stable performance. Self-recalibrating decoders aim to remove the burden this may present in the clinic by training themselves autonomously during normal use but have only been developed for continuous control. Here we address the problem for discrete decoding (classifiers). Approach. We recorded threshold crossings from 96-electrode arrays implanted in the motor cortex of two rhesus macaques performing center-out reaches in 7 directions over 41 and 36 separate days spanning 48 and 58 days in total for offline analysis. Main results. We show that for the purposes of developing a self-recalibrating classifier, tuning parameters can be considered as fixed within days and that parameters on the same electrode move up and down together between days. Further, drift is constrained across time, which is reflected in the performance of a standard classifier which does not progressively worsen if it is not retrained daily, though overall performance is reduced by more than 10% compared to a daily retrained classifier. Two novel self-recalibrating classifiers produce a similar to 15% increase in classification accuracy over that achieved by the non-retrained classifier to nearly recover the performance of the daily retrained classifier. Significance. We believe that the development of classifiers that require no daily retraining will accelerate the clinical translation of BCI systems. Future work should test these results in a closed-loop setting.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available