Jonas B. Zimmermann ae3791aa82 gin commit from L-1010036236 3 年之前
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README.md ae3791aa82 gin commit from L-1010036236 3 年之前

README.md

CereConn {#mainpage}

This is a wrapper around CereLink and cbhwlib, to facilitate data transfer from Blackrock NSP hardware in a setup that requires continuous flow. This C++ library was written with tight Python interactions in mind.

Installation

Assuming that CereLink has been installed to /opt/CereLink/, and that there is a conda environment running, set up the build environment with

cmake -H. -B_builds/Release -DCMAKE_INSTALL_PREFIX="/opt/CereLink/" -Drun_tests=ON -DPYTHON="$CONDA_PREFIX/bin/python" -DCMAKE_BUILD_TYPE=Release

and build and install with

cmake --build _builds/Release --target install

(or cd to _builds/Release and run make and make install).

There are unit tests that can be run with

make test

Note that some tests may require an NSP or NPlayServer on the network, sending data, and configured with included settings (e.g. tests/CereConn/test_NSP_settings_01.ccf).

In addition, there are Python tests, in python/tests.

Usage

For use of cc::CereConn in C++ projects, look at tests/CereConn/testCereConnRecord.cpp.

For use of cere_conn.CereConn cere_conn , the Python module, look at python/tests.

Info

Author: Jonas B. Zimmermann

Release History

  • 0.3.2 15 Jul 2019: Added averaging over history (smoothing) for Spike Band Power
  • 0.3.1.1 08 Apr 2019: Fix missing headers
  • 0.3.1 07 Apr 2019: Added Common Average Referencing for continuous and SBP data. See set_car_channels()
  • 0.3.0 04 Apr 2019: Refactored continuous and SBP data. Breaking changes: 1-based channel IDs used throughout, SBP/SR estimation and continuous data have to be explicitly enabled. See documentation for cere_conn.
  • 0.2.1 31 Mar 2019: Add filter accessors for spike band power estimation in Python. Add digital event processing. Improve documentation.
  • 0.2.0 17 Dec 2018: Add spike band power estimation methods. See python/tests/spike_band_power/test_01.py for usage examples.
  • 0.1.1 22 Nov 2018: Add various Python bindings to change spike rate estimation methods.
  • 0.1.0 15 Nov 2018: Implement spike rate computation, an dPython access
  • 0.0.4 06 Sep 2018: First public release.
datacite.yml
Title KIAP BCI: a BCI framework for intracortical signals
Authors Vlachos,Ioannis;Wyss Center for Bio and Neuroengineering;ORCID:0000-0002-3319-1486
Tonin,Alessandro;Wyss Center for Bio and Neuroengineering; Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany
Espinosa,Arnau;Wyss Center for Bio and Neuroengineering;ORCID:0000-0002-3268-0907
Khalili-Ardali,Majid;Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany;ORCID:0000-0001-9154-6970
Zimmermann,Jonas B;Wyss Center for Bio and Neuroengineering;ORCID:0000-0001-9225-2595
Description KIAP BCI is a BCI framework mainly written in Python that receives electrophysiological data from Utah arrays and controls an auditory speller.
License BSD-3-Clause (https://opensource.org/licenses/BSD-3-Clause/)
References Ujwal Chaudhary, Ioannis Vlachos, Jonas B Zimmermann, Arnau Espinosa, Alessandro Tonin, Andres Jaramillo-Gonzalez, Majid Khalili-Ardali, Helge Topka, Jens Lehmberg, Gerhard M Friehs, Alain Woodtli, John P Donoghue, Niels Birbaumer: Verbal Communication using Intracortical Signals in a Completely Locked In-Patient. medRxiv 2020.06.10.20122408 [doi:10.1101/2020.06.10.20122408] (IsSupplementTo)
Funding Wyss Center for Bio and Neuroengineering
Keywords Neuroscience
Human
Intracortical Array
Utah Array
Spikes
BCI
ALS
Python
Resource Type Software