Skip to main content

Real-time framework integrated with MNE-Python for online neuroscience research through LSL-compatible devices.

Project description

Ruff Code style: black Imports: isort codecov tests doc PyPI version Downloads Conda Version Conda Downloads Conda Platforms status

logo

MNE-LSL (Documentation website) 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.

Install

MNE-LSL supports python ≥ 3.9 and is available on PyPI and on conda-forge. Install instruction can be found on the documentation website.

Acknowledgment

MNE-LSL is based on BSL and NeuroDecode. The original version developed by Kyuhwa Lee was recognised at Microsoft Brain Signal Decoding competition with the First Prize Award (2016). MNE-LSL is based on the refactor version, BSL by Mathieu Scheltienne and Arnaud Desvachez for the Fondation Campus Biotech Geneva (FCBG) and development is still supported by the Fondation Campus Biotech Geneva (FCBG).

Copyright and license

The code is released under the BSD 3-Clause License.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mne_lsl-1.6.0.tar.gz (113.7 kB view details)

Uploaded Source

Built Distribution

mne_lsl-1.6.0-py3-none-any.whl (158.8 kB view details)

Uploaded Python 3

File details

Details for the file mne_lsl-1.6.0.tar.gz.

File metadata

  • Download URL: mne_lsl-1.6.0.tar.gz
  • Upload date:
  • Size: 113.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.9

File hashes

Hashes for mne_lsl-1.6.0.tar.gz
Algorithm Hash digest
SHA256 94485d798591dfbd432ef488643059ed1e32d0a6de869b3c80dcecb6862df63f
MD5 2dcbedbff0867797071f16f6f26bf9ea
BLAKE2b-256 54f11ffd260ffc2a9ba1e93c991b7543da897767b7009e370e83b8b3f3e769a6

See more details on using hashes here.

File details

Details for the file mne_lsl-1.6.0-py3-none-any.whl.

File metadata

  • Download URL: mne_lsl-1.6.0-py3-none-any.whl
  • Upload date:
  • Size: 158.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.9

File hashes

Hashes for mne_lsl-1.6.0-py3-none-any.whl
Algorithm Hash digest
SHA256 cd5ca7a40c3aad5b7a64949313af9adcea86e6962d69bb63c61621385ff8da86
MD5 195538812e9b2fde7af9d02a731eba9a
BLAKE2b-256 65c96bf6263d0b95e5783693f8fa130eacfff22766763ac43a2deaace05ef48a

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page