ASR Examples#

Examples demonstrating Artifact Subspace Reconstruction (ASR) for burst-artifact repair in EEG (and MEG) data — from a basic clean to a full preprocessing pipeline, with each method example grounded in its source paper.

Getting started#

plot_01_asr_basics.py: Standard ASR on synthetic multichannel bursts.
plot_02_mne_raw_qc.py: MNE Raw usage with repair annotations and an optional clean_windows-style final rejection mask.
plot_05_asr_visualization.py: The ASR-specific mne_denoise.viz plots (repair timeline, component reconstruction, calibration fraction) alongside the generic before/after and PSD helpers.

Cutoff and variants#

plot_06_cutoff_tuning.py: How cutoff trades data modified against variance removed (Chang 2020).
plot_07_riemannian_asr.py: Riemannian (method="riemannian_windowed") vs standard ASR on real blinks (Blum 2019).
plot_03_adaptive_asr.py: AASR-style streaming with fit / partial_fit / transform.
plot_08_adaptive_variants.py: Adaptive psp vs psw vs mw on non-stationary data, with the moving-window adaptation trajectory (Tsai).
plot_04_juggler_asr.py: Juggler DBSCAN calibration on dense short bursts.
plot_09_juggler_strategies.py: Juggler dbscan vs gev reference selection under heavy contamination (Kim 2025).
plot_10_choosing_a_variant.py: Standard vs Riemannian vs Juggler on one substrate, with a short recommendation.

I/O, QC, and pipelines#

plot_11_epochs_and_meg.py: ASR on mne.Epochs and on MEG magnetometers.
plot_12_diagnostics_qc.py: get_diagnostics / variance_removed / to_annotations and the three ASR diagnostic plots.
plot_13_pipeline_filter_asr_ica.py: A realistic filter -> ASR -> ICA workflow on real EEG.

Experimental research prototypes#

plot_15_guided_asr.py: DSS-guided soft ASR (GuidedASR), demonstrated on synthetic data only.

Warning

GuidedASR is an unpublished, unvalidated research prototype. Its current evidence is limited to unit tests and synthetic benchmarks. Do not treat it as a validated EEG preprocessing method, and independently verify signal preservation and artifact attenuation for your data.

Notes#

Most examples use synthetic data so they run without downloads; plot_07 and plot_13 (and the MEG part of plot_11) use the MNE sample dataset. Apply ASR to real EEG only after bad-channel handling, referencing, and high-pass filtering in the surrounding MNE workflow.

Artifact Subspace Reconstruction: Basic Usage.

Artifact Subspace Reconstruction: Raw QC and Annotations.

Adaptive ASR with chunk updates

JugglerASR for dense short bursts

Visualizing ASR with mne_denoise.viz

Choosing the ASR cutoff.

Riemannian ASR versus standard ASR.

Adaptive ASR variants (PSP / PSW / MW).

Juggler ASR: DBSCAN vs GEV reference selection.

Choosing an ASR variant.

ASR on Epochs and on MEG.

ASR diagnostics and quality control.

A realistic pipeline: filter, ASR, then ICA.

Quantifying Dataset Noise with RMS Statistics

Guided ASR: preserving neural activity that ASR would over-clean.