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mne-denoise 0.0.1 documentation

  • Getting started
  • DSS Module Documentation
  • TimeShiftDSS
  • Artifact Subspace Reconstruction
  • Reference-free BSS-CCA
    • Sensor Noise Suppression (SNS)
    • Singular Spectrum Analysis
    • Examples Gallery
    • API reference
    • Development workflow
  • GitHub
  • Getting started
  • DSS Module Documentation
  • TimeShiftDSS
  • Artifact Subspace Reconstruction
  • Reference-free BSS-CCA
  • Sensor Noise Suppression (SNS)
  • Singular Spectrum Analysis
  • Examples Gallery
  • API reference
  • Development workflow
  • GitHub

Section Navigation

  • Sensor Noise Suppression
    • Basic Sensor Noise Suppression
    • SNS assumptions and diagnostics
  • Reference-free BSS-CCA
    • Basic reference-free BSS-CCA
    • BSS-CCA algorithm properties
  • Singular Spectrum Analysis
    • Basic SSA decomposition and frequency-guided cleaning
    • Local SSA properties on a synthetic signal
  • DSS Examples
    • Fundamentals of DSS.
    • Artifact Correction with DSS.
    • Denoising Evoked Responses.
    • Denoising Rhythms (Spectral DSS).
    • Periodic Signals (SSVEP and Quasi-Periodic).
    • Temporal Biases for Ordinary DSS.
    • Time-Frequency DSS: Spectrogram Masking.
    • Blind Source Separation and ICA Equivalence.
    • Custom DSS: Defining Your Own Bias.
    • Efficiency Benchmark: DSS vs PCA, ICA, and Averaging.
    • Adaptive Wiener Masking for Bursty Signals.
    • Joint DSS (Multi-Dataset Repeatability).
    • Cardiac DSS as an Explicit Composition
  • ZapLine Examples
    • ZapLine: Line Noise Removal Fundamentals.
    • ZapLine: Parameter Tuning and Real Data.
    • ZapLine: Epoched Data and Real Data Examples.
    • ZapLine-plus: Adaptive Cleaning on Non-Stationary Noise.
    • ZapLine-plus: Advanced Settings and Features.
    • ZapLine on MEG-like data with many co-equal noise components.
  • ASR Examples
    • 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.
  • Spectrum Interpolation Examples
    • Spectrum Interpolation: Power-Line Noise Removal.
  • Examples Gallery
  • Sensor Noise Suppression

Sensor Noise Suppression#

Examples of Sensor Noise Suppression on NumPy and MNE data, followed by deterministic demonstrations of the algorithm’s assumptions and diagnostics.

Basic Sensor Noise Suppression

Basic Sensor Noise Suppression

SNS assumptions and diagnostics

SNS assumptions and diagnostics

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Basic Sensor Noise Suppression
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