학술논문

Decoding Local Field Potentials for Neural Interfaces
Document Type
Periodical
Source
IEEE Transactions on Neural Systems and Rehabilitation Engineering IEEE Trans. Neural Syst. Rehabil. Eng. Neural Systems and Rehabilitation Engineering, IEEE Transactions on. 25(10):1705-1714 Oct, 2017
Subject
Bioengineering
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Communication, Networking and Broadcast Technologies
Decoding
Electrodes
Biological control systems
Firing
Electric potential
Neurons
Feature extraction
Biofeedback
brain–machine interface (BMI)
decoding
local field potentials (LFPs)
Language
ISSN
1534-4320
1558-0210
Abstract
The stability and frequency content of local field potentials (LFPs) offer key advantages for long-term, low-power neural interfaces. However, interpreting LFPs may require new signal processing techniques which should be informed by a scientific understanding of how these recordings arise from the coordinated activity of underlying neuronal populations. We review current approaches to decoding LFPs for brain–machine interface (BMI) applications, and suggest several directions for future research. To facilitate an improved understanding of the relationship between LFPs and spike activity, we share a dataset of multielectrode recordings from monkey motor cortex, and describe two unsupervised analysis methods we have explored for extracting a low-dimensional feature space that is amenable to biomimetic decoding and biofeedback training.