@inproceedings{f1f5bfbf3f4d487eb2d13b3f696af01e,
title = "Design of a neural decoder by sensory prediction and error correction",
abstract = "Brain-machine interfaces (BMI) hold great potential to improve the quality of life of many patients with disabilities. The neural decoder, which expresses the mapping between the neural signals and the subject's motion, plays an important role in BMI systems. Conventional neural decoders are generally in the form of a kinematic Kalman filter which does not possess an explicit mechanism to deal with the unavoidable mismatch between the biological system and the model of the system used by the decoder. This paper presents a novel design of a neural decoder that uses a one-step model predictive controller to generate a control signal that compensates for the inherent model mismatch. The effectiveness of the proposed decoding algorithm compares favorably to the state-of-the-art Kalman filter in numerical simulations with different degrees of model mismatch.",
author = "Junkai Lu and Mo Chen and Chang, {Young Hwan} and Masayoshi Tomizuka and Carmena, {Jose M.} and Tomlin, {Claire J.}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 ; Conference date: 15-12-2014 Through 17-12-2014",
year = "2014",
doi = "10.1109/CDC.2014.7040489",
language = "English (US)",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "February",
pages = "6999--7004",
booktitle = "53rd IEEE Conference on Decision and Control,CDC 2014",
edition = "February",
}