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ElectroCUDA

Robust electrophysiology tools with GPU acceleration

Install / Use

/learn @kevmtan/ElectroCUDA

README

electroCUDA – see wiki for documentation & theoretical overview

<a href="https://github.com/kevmtan/electroCUDA/blob/master/wiki/s38_ic84_spec.jpg?raw=true"><img src="https://github.com/kevmtan/electroCUDA/blob/master/wiki/s38_ic84_spec.jpg?raw=true" alt="Summary statistics of a highly-localized neuronal source decomposed by electroCUDA" width="800"/></a> <br><sub> Summary statistics of an independent neuronal source decomposed by electroCUDA </sub>

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ElectroCUDA – robust preprocessing & analysis for electrophysiology. Core features include noise-resistant signal processing, robust statistics & extensive hardware acceleration.

ElectroCUDA is intended for any multichannel field potential recordings (LFP/EEG/MEG), but development has focused on intracranial EEG (ECoG/sEEG) thus far.

Code is Matlab-based with calls to compiled CUDA, C/C++ & Fortran binaries. User-friendly wrappers abstract away all non-Matlab operations. Modular code & data structures facilitate easy interoperability with other packages. Compute performance is maximized via layered hardware acceleration & comprehensive code optimizations.

Development status: <span style="color: red;"> PRE-ALPHA </span>

⚠️   Code is not yet validated or peer-reviewed for general use

Acknowledgements

This work was supported by National Science Foundation Graduate Research Fellowship DGE-1650604 and Department of Defense Grant 13RSA281. See wiki for full acknowledgments.

License

ElectroCUDA is free and open-source under GNU GPL 3.0

Terms of use

Use this code at your own risk. Users assume full responsibility for any eventuality related to this code. This code is for research purposes only and is not intended for clinical or medical use.

USE AND DISTRIBUTION OF THIS SOFTWARE MAY BE SUBJECT TO UNIVERSITY OF CALIFORNIA INTELLECTUAL PROPERTY RIGHTS AND UNITED STATES MANDATES FOR FEDERALLY-FUNDED RESEARCH.

THE CONTENT HEREIN IS PROVIDED "AS IS" WITHOUT ANY EXPRESS OR IMPLIED WARRANTIES. IN NO EVENT SHALL THE AUTHORS AND CONTRIBUTORS OF CONTENT HEREIN BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY OR CONSEQUENTIAL DAMAGES AND/OR ADVERSE OUTCOMES RELATED IN ANY WAY TO THE USE OF THIS CONTENT. ANY USE OF THIS CONTENT IMPLIES ACCEPTANCE OF THESE TERMS.

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GitHub Stars21
CategoryDevelopment
Updated6d ago
Forks1

Languages

Cuda

Security Score

95/100

Audited on Mar 26, 2026

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