GhostiPy: An Efficient Signal Processing and Spectral Analysis Toolbox for Large Data
| dc.citation.issueNumber | 6 | en_US |
| dc.citation.journalTitle | eNeuro | en_US |
| dc.citation.volumeNumber | 8 | en_US |
| dc.contributor.author | Chu, Joshua P. | en_US |
| dc.contributor.author | Kemere, Caleb T. | en_US |
| dc.date.accessioned | 2021-12-15T22:10:18Z | en_US |
| dc.date.available | 2021-12-15T22:10:18Z | en_US |
| dc.date.issued | 2021 | en_US |
| dc.description.abstract | Recent technological advances have enabled neural recordings consisting of hundreds to thousands of channels. As the pace of these developments continues to grow rapidly, it is imperative to have fast, flexible tools supporting the analysis of neural data gathered by such large-scale modalities. Here we introduce GhostiPy (general hub of spectral techniques in Python), a Python open source software toolbox implementing various signal processing and spectral analyses including optimal digital filters and time–frequency transforms. GhostiPy prioritizes performance and efficiency by using parallelized, blocked algorithms. As a result, it is able to outperform commercial software in both time and space complexity for high-channel count data and can handle out-of-core computation in a user-friendly manner. Overall, our software suite reduces frequently encountered bottlenecks in the experimental pipeline, and we believe this toolset will enhance both the portability and scalability of neural data analysis. | en_US |
| dc.identifier.citation | Chu, Joshua P. and Kemere, Caleb T.. "GhostiPy: An Efficient Signal Processing and Spectral Analysis Toolbox for Large Data." <i>eNeuro,</i> 8, no. 6 (2021) Society for Neuroscience: https://doi.org/10.1523/ENEURO.0202-21.2021. | en_US |
| dc.identifier.digital | ENEURO-0202-21-2021-full | en_US |
| dc.identifier.doi | https://doi.org/10.1523/ENEURO.0202-21.2021 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/111791 | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Society for Neuroscience | en_US |
| dc.rights | This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed. | en_US |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
| dc.title | GhostiPy: An Efficient Signal Processing and Spectral Analysis Toolbox for Large Data | en_US |
| dc.type | Journal article | en_US |
| dc.type.dcmi | Text | en_US |
| dc.type.publication | publisher version | en_US |
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