.png){kind=icon}
.png){kind=icon}
Return
Recording and Decoding for Neural Prostheses
.png){kind=icon}
PRE
AI
DOI：10.1109/JPROC.2015.2507180.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper reviews technologies and signal processing algorithms for decoding peripheral nerve and electrocorticogram signals to interpret human intent and control prosthetic arms. The review includes a discussion of human motor system physiology and physiological signals that can be used to decode motor intent, electrode technology for acquiring neural data, and signal processing methods including decoders based on Kalman filtering and least-squares regressors. Representative results from human experiments demonstrate the progress that has been made in neural decoding and its potential for developing neuroprosthetic arms that act and feel like natural arms.
Keywords:
Brain-computer interface
electrocorticogram
Kalman decoders
neural decoding
neuroprosthetics
peripheral nerve signals
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
25.9
Papers:
9.9K
Citations:
4.5W
Organization
View more organizations