.png){kind=icon}
返回
Making brain-machine interfaces robust to future neural variability
.png){kind=icon}
OA
AI
DOI：10.1038/ncomms13749.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A major hurdle to clinical translation of brain-machine interfaces (BMIs) is that current decoders, which are trained from a small quantity of recent data, become ineffective when neural recording conditions subsequently change. We tested whether a decoder could be made more robust to future neural variability by training it to handle a variety of recording conditions sampled from months of previously collected data as well as synthetic training data perturbations. We developed a new multiplicative recurrent neural network BMI decoder that successfully learned a large variety of neural-to-kinematic mappings and became more robust with larger training data sets. Here we demonstrate that when tested with a non-human primate preclinical BMI model, this decoder is robust under conditions that disabled a state-of-the-art Kalman filter-based decoder. These results validate a new BMI strategy in which accumulated data history are effectively harnessed, and may facilitate reliable BMI use by reducing decoder retraining downtime.
Keyword:
LOCAL-FIELD POTENTIALS
MOTOR CORTEX
PREMOTOR
MOVEMENTS
DISCHARGE
FEEDBACK
PEOPLE
SPACE
TERM
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
15.7
论文数:
9.4W
被引数:
91.2W
机构
引用论文
First attempt to obtain the bulk composition of ancient silver–copper coins by using XRF and GRT
PREAI
分享收藏
Information Systems Opportunities in Brain-Machine Interface Decoders脑机接口解码器中的信息系统机会.png)
PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
分享收藏Ten-dimensional anthropomorphic arm control in a human brain-machine interface: difficulties, solutions, and limitations人脑-机接口中的十维拟人化手臂控制: 困难，解决方案和局限性
PREAI
分享收藏
查看更多内容