[Eeglablist] MNE-RT: an open-source real-time neurofeedback/BCI framework

[payam sadeghi]

Hi all,

I’d like to introduce MNE-RT (link <https://urldefense.com/v3/__https://github.com/mne-rt-org/mne-rt__;!!Mih3wA!D9cNeQ9Rktz8PwA54hWSS_5SHSQyv-nqUVEVNj9QpeoBig0cVIcwnAExdSrqTNYhCnbggtQYcV4KtxsTt3REj8nz4xlGRRA$ >),
an open-source Python package for real-time M/EEG signal processing, built
on top of MNE-Python and MNE-LSL. It covers the entire closed-loop pipeline
in a single, researcher-friendly API, aimed at neurofeedback, BCI, and
real-time clinical/basic-science monitoring.

What it does:

   - 21 real-time neural feature modalities in both sensor and source space
   - Real-time single-trial decoding (CSP + any scikit-learn classifier)
   - Adaptive feedback protocols: z-score, threshold, percentile,
   staircase, operant/RL-based, sham, multi-band, and cross-session transfer
   - Online artifact-correction methods: ASR, adaptive LMS, GEDAI, ORICA,
   real-time Maxwell/SSS filtering for MEG
   - Live visualization windows: Raw signal, NF feedback curves, epoch
   overlays, scalp topographies, 3D brain activity, TFR heatmaps, …
   - External feedback output via OSC (Max/MSP, SuperCollider) and LSL
   outlets (PsychoPy, OpenViBE, BCI2000)
   - BIDS-compatible session saving
   - Full CLI

Use cases it’s built for: neurofeedback research, real-time
BCI/motor-imagery decoding, and any closed-loop paradigm needing live
feature extraction + adaptive feedback + artifact correction in one place.

Any feedback, issues, feature requests, or contributions, especially from
anyone doing real-time work who can stress-test it against their own
hardware/paradigms are welcome!

Thanks!
Payam