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Decoding Local Field Potentials for Neural Interfaces
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...
Formato: | Online Artículo Texto |
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Lenguaje: | English |
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IEEE
2016
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6051483/ https://www.ncbi.nlm.nih.gov/pubmed/28113942 http://dx.doi.org/10.1109/TNSRE.2016.2612001 |
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collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6051483 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | IEEE |
record_format | MEDLINE/PubMed |
spelling | pubmed-60514832018-11-15 Decoding Local Field Potentials for Neural Interfaces IEEE Trans Neural Syst Rehabil Eng Article 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. IEEE 2016-11-14 /pmc/articles/PMC6051483/ /pubmed/28113942 http://dx.doi.org/10.1109/TNSRE.2016.2612001 Text en This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/ http://creativecommons.org/licenses/by/3.0/ |
spellingShingle | Article Decoding Local Field Potentials for Neural Interfaces |
title | Decoding Local Field Potentials for Neural Interfaces |
title_full | Decoding Local Field Potentials for Neural Interfaces |
title_fullStr | Decoding Local Field Potentials for Neural Interfaces |
title_full_unstemmed | Decoding Local Field Potentials for Neural Interfaces |
title_short | Decoding Local Field Potentials for Neural Interfaces |
title_sort | decoding local field potentials for neural interfaces |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6051483/ https://www.ncbi.nlm.nih.gov/pubmed/28113942 http://dx.doi.org/10.1109/TNSRE.2016.2612001 |
work_keys_str_mv | AT decodinglocalfieldpotentialsforneuralinterfaces AT decodinglocalfieldpotentialsforneuralinterfaces |