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Decoding local field potentials for neural interfaces

This dataset is associated with the paper, Jackson & Hall 2016, which is open source, and can be found here: http://ieeexplore.ieee.org/document/7742994/

The DataPort Repository contains the data used primarily for generating Figure 1.

ABSTRACT: The stability and frequency content of the local field potentials (LFP) 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.

Instructions: 

** Please note that this is under construction, and all data and code is still being uploaded whilst this notice is present. Thank-you. Tom **

All code is hosted as a GIT repository (below), as well as instructions, which can be found by clicking on the link/file called README.md in that repository.

https://github.com/thomasmhall-newcastle/IEEE-TNSRE-2016-lfLFPs

You are free to clone/pull this repository and use it under MIT license, on the understanding that any use of this code will be acknowledged by citing the original paper, DOI: 10.1109/TNSRE.2016.2612001, which is Open Access and can be found here: http://ieeexplore.ieee.org/document/7742994/

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Uploaded MonkeyR_data.zip (containing 15 MAT files)

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OPEN ACCESS Dataset Details

Citation Author(s):
Andrew Jackson, Thomas M. Hall
Submitted by:
Thomas Hall
Last updated:
Thu, 10/12/2017 - 13:48
DOI:
10.21227/H2VW5Z
Data Format:
Links:
 
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[1] Andrew Jackson, Thomas M. Hall, "Decoding local field potentials for neural interfaces", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2VW5Z. Accessed: Oct. 23, 2017.
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doi = {10.21227/H2VW5Z},
url = {http://dx.doi.org/10.21227/H2VW5Z},
author = {Andrew Jackson; Thomas M. Hall },
publisher = {IEEE Dataport},
title = {Decoding local field potentials for neural interfaces},
year = {2017} }
TY - DATA
T1 - Decoding local field potentials for neural interfaces
AU - Andrew Jackson; Thomas M. Hall
PY - 2017
PB - IEEE Dataport
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Andrew Jackson, Thomas M. Hall. (2017). Decoding local field potentials for neural interfaces. IEEE Dataport. http://dx.doi.org/10.21227/H2VW5Z
Andrew Jackson, Thomas M. Hall, 2017. Decoding local field potentials for neural interfaces. Available at: http://dx.doi.org/10.21227/H2VW5Z.
Andrew Jackson, Thomas M. Hall. (2017). "Decoding local field potentials for neural interfaces." Web.
1. Andrew Jackson, Thomas M. Hall. Decoding local field potentials for neural interfaces [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2VW5Z
Andrew Jackson, Thomas M. Hall. "Decoding local field potentials for neural interfaces." doi: 10.21227/H2VW5Z