API Reference#

This page provides the complete API reference for MNE-RT.

Core#

`mne_rt.RTStream`	Real-time Real-time M/EEG session controller.
`mne_rt.RTEpochs`	Event-triggered epoch accumulator backed by `mne_lsl.stream.EpochsStream`.

`mne_rt.viz.NFPlot`	Scrolling real-time neurofeedback signal monitor.
`mne_rt.viz.RawPlot`	Scrolling raw M/EEG channel viewer.
`mne_rt.viz.EpochPlot`	Real-time scrolling M/EEG viewer with epoch / trigger overlays.
`mne_rt.viz.BrainPlot`	Interactive real-time 3D brain activation display.
`mne_rt.viz.TopomapPlot`	Real-time scalp topomap showing per-band power distribution.
`mne_rt.viz.TopoPlot`	Real-time scalp-layout ERP display.
`mne_rt.viz.ButterflyPlot`	Real-time butterfly plot: all EEG/MEG channels overlaid per condition.
`mne_rt.viz.TFRPlot`	Real-time time-frequency representation (TFR).
`mne_rt.viz.CompareEvoked`	Real-time per-channel condition overlay with SEM shading and peak markers.

`mne_rt.tools.AdaptiveLMSFilter`	Adaptive LMS filter for real-time EOG / ECG artifact removal.
`mne_rt.tools.ORICA`	Online Recursive ICA (ORICA) for EEG data.
`mne_rt.tools.GEDAIDenoiser`	Artifact removal via Generalised Eigendecomposition-based Artifact Identification (GEDAI).
`mne_rt.tools.ASRDenoiser`	Artifact Subspace Reconstruction (ASR) for streaming EEG.
`mne_rt.tools.RTMaxwellFilter`	Real-time Maxwell filtering (SSS / tSSS) for streaming MEG data.

`mne_rt.tools.BadChannelDetector`	Multi-criterion real-time bad channel detector for streaming M/EEG.
`mne_rt.tools.RiemannianPotatoDetector`	Online EEG/MEG artifact detector based on the Riemannian Potato.

See NF Protocols for the full protocol guide with formulas and examples.

`mne_rt.protocols.ThresholdProtocol`	Threshold-based NF reward protocol with optional adaptive threshold.
`mne_rt.protocols.ZScoreProtocol`	Z-score feedback protocol with rolling baseline normalisation.
`mne_rt.protocols.PercentileProtocol`	Percentile-based NF reward protocol with rolling history.
`mne_rt.protocols.LinearTrendProtocol`	Reward protocol that detects a statistically significant NF trend.
`mne_rt.protocols.ShamProtocol`	Wraps any NF protocol with sham (double-blind) feedback.
`mne_rt.protocols.UpDownStaircaseProtocol`	Up-down adaptive staircase threshold protocol.
`mne_rt.protocols.MultiBandProtocol`	Reward protocol for simultaneous two-band control.
`mne_rt.protocols.RLProtocol`	Adaptive NF protocol with reinforcement-learning threshold updates.
`mne_rt.protocols.OperantProtocol`	Operant conditioning reinforcement-schedule wrapper.
`mne_rt.protocols.TransferProtocol`	Cross-session transfer NF protocol seeded from a prior-session file.

Reduce multiple parallel NF feature values to a single mixed feedback score. See FeatureCombiner for the base-class interface.

`mne_rt.combiners.FeatureCombiner`	Abstract base class for multi-feature NF combiners.
`mne_rt.combiners.WeightedSumCombiner`	Weighted linear combination of feature values.
`mne_rt.combiners.GeometricMeanCombiner`	Geometric mean of (positive) feature values.
`mne_rt.combiners.ZScoredNormCombiner`	Euclidean norm after online z-score normalisation of each feature.
`mne_rt.combiners.LearnedCombiner`	Data-driven combination via a fitted sklearn-compatible estimator.

`mne_rt.OSCSender`	Thread-safe OSC client that broadcasts NF feature values.
`mne_rt.LSLSender`	Thread-safe LSL outlet that broadcasts NF feature values.

`mne_rt.tools.simulate_raw`	Generate a synthetic EEG or MEG recording with a sinusoidal source.
`mne_rt.tools.simulate_nf_session`	Generate a realistic multi-artifact EEG simulation for NF testing.
`mne_rt.modalities.ModalityMixin`	Feature-extraction engine for all ANT NF modalities.

Save a neurofeedback session in BIDS format.

Set the ANT (and MNE) logging level.