Decoding local field potentials for neural interfaces
- Citation Author(s):
- Andrew Jackson, Thomas M. Hall
- Submitted by:
- Thomas Hall
- Last updated:
- Tue, 05/17/2022 - 22:21
- Data Format:
- Link to Paper:
- Creative Commons Attribution
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.
** 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.
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/
- MonkeyR_data.zip (84.73 MB)
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Uploaded MonkeyR_data.zip (containing 15 MAT files)