Difference between revisions of "DataHub"
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| + | more info: [https://github.com/labstreaminglayer/liblsl-Python cross platform pylsl] | ||
===Versions=== | ===Versions=== | ||
Revision as of 09:21, 1 February 2022
| Developer(s) | Christian Kothe; University of California San Diego. | ||||
|---|---|---|---|---|---|
| Development status | in development | ||||
| Written in | C, C++, Python, Java, C#, MATLAB | ||||
| Operating system | Windows, Linux, MacOS, Android, iOS | ||||
| Type | Data collection | ||||
| License | Open source | ||||
| Website | LSL webpage | ||||
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The DataHub Makes use of the lab streaming layer. The lab streaming layer (LSL) is a system for the unified collection of measurement time series in research experiments that handles both the networking, time-synchronization, (near-) real-time access as well as optionally the centralized collection, viewing and disk recording of the data.
Installation
Our support for LSL is mainly done in python. Download python here: Please choose a 64 bit version.. Run the installer and make sure to add Python to the file path (it's an option in the installer).
Open a command prompt, start with upgrading the pip installer by typing:
python -m pip install --upgrade pip
Then:
pip install pylsl
more info: cross platform pylsl
Versions
Unity is constantly updated, and new versions will frequently include Beta features that may not be super reliable and well documented yet. As such it is sometimes quite difficult to figure out which version to use. If you wish to create your own Unity projects, we advise to install the latest long term support (LTS) release. Beware that any tutorials, forum answers and plugins you find online may no longer be compatible with your version, so always check the date and official documentation. Of course you can also come to the TSG for help.
Usage
(Under Construction)
We are working on templates and tips. Stay tuned!
Builds
We advise not to run your experiment from the Unity Editor, this will cause unwanted overhead that harms the performance. You can create a PC Standalone build to run it on our lab computers.
References