Robust neural decoding of reaching movements for prosthetic systems

被引:4
作者
Kemere, C [1 ]
Sahani, M [1 ]
Meng, T [1 ]
机构
[1] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA
来源
PROCEEDINGS OF THE 25TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-4: A NEW BEGINNING FOR HUMAN HEALTH | 2003年 / 25卷
关键词
neural prosthetics; hidden Markov models;
D O I
10.1109/IEMBS.2003.1280146
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
A new neural prosthetic decoder architecture is presented which uses a hidden Markov model of typical arm movements to assist the reconstruction of intended trajectories from an ensemble of neural signals. The use of such a model results in a decoder which is robust to fewer or smaller neural signals. With limited information, the average error of the reconstructed trajectories produced by the robust decoder is half of that produced by the standard linear filter approach.
引用
收藏
页码:2079 / 2082
页数:4
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