Decoding local field potentials for neural interfaces

Citation Author(s):
Andrew Jackson, Thomas M. Hall
Submitted by:
Thomas Hall
Last updated:
Sat, 06/16/2018 - 23:05
Data Format:
Creative Commons Attribution
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This dataset is associated with the paper, Jackson & Hall 2016, which is open source, and can be found here:

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.


** 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 in that repository.

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:


Uploaded (containing 15 MAT files)

Submitted by Thomas Hall on Thu, 10/12/2017 - 13:49