Self-recalibrating classifiers for intracortical brain-computer interfaces.
Self-recalibrating classifiers for intracortical brain-computer interfaces.
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DOI：
10.1088/1741-2560/11/2/026001
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发表时间：
2014-04
影响因子：
4
通讯作者：
Yu BM
中科院分区：
文献类型：
--
作者：
Bishop W;Chestek CC;Gilja V;Nuyujukian P;Foster JD;Ryu SI;Shenoy KV;Yu BM
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). 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. 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 ~15% increase in classification accuracy over that achieved by the non-retrained classifier to nearly recover the performance of the daily retrained classifier. 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.
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影响因子：
4
作者：
Chestek CA;Gilja V;Nuyujukian P;Foster JD;Fan JM;Kaufman MT;Churchland MM;Rivera-Alvidrez Z;Cunningham JP;Ryu SI;Shenoy KV
通讯作者：
Shenoy KV
偏差，最佳线性估计以及基于峰值的大脑计算机界面算法的开环模拟与闭环性能之间的差异。
DOI：
10.1016/j.neunet.2009.05.005
发表时间：
2009-11
期刊：
Neural networks : the official journal of the International Neural Network Society
影响因子：
--
作者：
Chase SM;Schwartz AB;Kass RE
通讯作者：
Kass RE
DOI：
10.1073/pnas.0808113105
发表时间：
2008-12-09
影响因子：
11.1
作者：
Jarosiewicz, Beata;Chase, Steven M.;Schwartz, Andrew B.
通讯作者：
Schwartz, Andrew B.
影响因子：
9.8
作者：
Ganguly K;Carmena JM
通讯作者：
Carmena JM
影响因子：
4.7
作者：
Galan, F.;Nuttin, M.;Millan, J. del R.
通讯作者：
Millan, J. del R.