| Name | mne-lsl JSON |
| Version |
1.3.1
JSON |
| download |
| home_page | None |
| Summary | Real-time framework integrated with MNE-Python for online neuroscience research through LSL-compatible devices. |
| upload_time | 2024-04-03 12:53:30 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | Copyright © 2023-2024, authors of MNE-LSL All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
brain
eeg
eeg
electroencephalography
labstreaminglayer
lsl
neuroimaging
neuroscience
python
real-time
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://github.com/astral-sh/ruff)
[](https://github.com/psf/black)
[](https://pycqa.github.io/isort/)
[](https://codecov.io/gh/mne-tools/mne-lsl)
[](https://github.com/mne-tools/mne-lsl/actions/workflows/pytest.yaml)
[](https://github.com/mne-tools/mne-lsl/actions/workflows/doc.yaml)
[](https://badge.fury.io/py/mne-lsl)
[](https://pepy.tech/project/mne-lsl)
[](https://anaconda.org/conda-forge/mne-lsl)
[](https://anaconda.org/conda-forge/mne-lsl)
[](https://anaconda.org/conda-forge/mne-lsl)
<img align="right" src="https://raw.githubusercontent.com/mne-tools/mne-lsl/main/doc/_static/logos/logo-mne-hex.svg" alt="logo" width="200"/>
**MNE-LSL** [(Documentation website)](https://mne.tools/mne-lsl)
provides a real-time brain signal streaming framework.
**MNE-LSL** contains an improved python-binding for the Lab Streaming Layer C++ library,
`mne_lsl.lsl`, replacing `pylsl`. This low-level binding is used in high-level objects
to interact with LSL streams.
Any signal acquisition system supported by native LSL or OpenVibe is also
supported by MNE-LSL. Since the data communication is based on TCP, signals can be
transmitted wirelessly. For more information about LSL, please visit the
[LSL github](https://github.com/sccn/labstreaminglayer).
# Install
MNE-LSL supports `python ≥ 3.9` and is available on
[PyPI](https://pypi.org/project/mne-lsl/) and on
[conda-forge](https://anaconda.org/conda-forge/mne-lsl).
Install instruction can be found on the
[documentation website](https://mne.tools/mne-lsl/stable/resources/install.html).
# Acknowledgment
<img align="right" src="https://raw.githubusercontent.com/mne-tools/mne-lsl/main/doc/_static/partners/fcbg-hnp-meeg.png" width=150>
**MNE-LSL** is based on **BSL** and **NeuroDecode**. The original version developed by
[**Kyuhwa Lee**](https://github.com/dbdq) was recognised at
[Microsoft Brain Signal Decoding competition](https://github.com/dbdq/microsoft_decoding)
with the First Prize Award (2016).
**MNE-LSL** is based on the refactor version, **BSL** by
[**Mathieu Scheltienne**](https://github.com/mscheltienne) and
[**Arnaud Desvachez**](https://github.com/dnastars) for the
[Fondation Campus Biotech Geneva (FCBG)](https://github.com/fcbg-hnp-meeg) and
development is still supported by the
[Human Neuroscience Platform (FCBG)](https://hnp.fcbg.ch/).
# Copyright and license
The code is released under the
[BSD 3-Clause License](https://opensource.org/license/bsd-3-clause/).
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"description": "[](https://github.com/astral-sh/ruff)\n[](https://github.com/psf/black)\n[](https://pycqa.github.io/isort/)\n[](https://codecov.io/gh/mne-tools/mne-lsl)\n[](https://github.com/mne-tools/mne-lsl/actions/workflows/pytest.yaml)\n[](https://github.com/mne-tools/mne-lsl/actions/workflows/doc.yaml)\n[](https://badge.fury.io/py/mne-lsl)\n[](https://pepy.tech/project/mne-lsl)\n[](https://anaconda.org/conda-forge/mne-lsl)\n[](https://anaconda.org/conda-forge/mne-lsl)\n[](https://anaconda.org/conda-forge/mne-lsl)\n\n<img align=\"right\" src=\"https://raw.githubusercontent.com/mne-tools/mne-lsl/main/doc/_static/logos/logo-mne-hex.svg\" alt=\"logo\" width=\"200\"/>\n\n**MNE-LSL** [(Documentation website)](https://mne.tools/mne-lsl)\nprovides a real-time brain signal streaming framework.\n**MNE-LSL** contains an improved python-binding for the Lab Streaming Layer C++ library,\n`mne_lsl.lsl`, replacing `pylsl`. This low-level binding is used in high-level objects\nto interact with LSL streams.\n\nAny signal acquisition system supported by native LSL or OpenVibe is also\nsupported by MNE-LSL. Since the data communication is based on TCP, signals can be\ntransmitted wirelessly. For more information about LSL, please visit the\n[LSL github](https://github.com/sccn/labstreaminglayer).\n\n# Install\n\nMNE-LSL supports `python \u2265 3.9` and is available on\n[PyPI](https://pypi.org/project/mne-lsl/) and on\n[conda-forge](https://anaconda.org/conda-forge/mne-lsl).\nInstall instruction can be found on the\n[documentation website](https://mne.tools/mne-lsl/stable/resources/install.html).\n\n# Acknowledgment\n\n<img align=\"right\" src=\"https://raw.githubusercontent.com/mne-tools/mne-lsl/main/doc/_static/partners/fcbg-hnp-meeg.png\" width=150>\n\n**MNE-LSL** is based on **BSL** and **NeuroDecode**. The original version developed by\n[**Kyuhwa Lee**](https://github.com/dbdq) was recognised at\n[Microsoft Brain Signal Decoding competition](https://github.com/dbdq/microsoft_decoding)\nwith the First Prize Award (2016).\n**MNE-LSL** is based on the refactor version, **BSL** by\n[**Mathieu Scheltienne**](https://github.com/mscheltienne) and\n[**Arnaud Desvachez**](https://github.com/dnastars) for the\n[Fondation Campus Biotech Geneva (FCBG)](https://github.com/fcbg-hnp-meeg) and\ndevelopment is still supported by the\n[Human Neuroscience Platform (FCBG)](https://hnp.fcbg.ch/).\n\n# Copyright and license\n\nThe code is released under the\n[BSD 3-Clause License](https://opensource.org/license/bsd-3-clause/).\n",
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