DSS Examples#

Overview#

Examples demonstrating Denoising Source Separation (DSS) across evoked, spectral, temporal, and blind-separation use cases.

Files#

  • plot_01_dss_fundamentals.py: Core DSS concepts with trial-average and bandpass biases.

  • plot_02_artifact_correction.py: Blink and heartbeat correction with DSS.

  • plot_03_evoked_responses.py: Evoked-response denoising and contrast-focused DSS.

  • plot_04_spectral_dss.py: Frequency-specific component extraction on synthetic and real data.

  • plot_05_periodic_dss.py: Periodic signal extraction for SSVEP and quasi-periodic structure.

  • plot_06_temporal_dss.py: Time-shift and smoothness biases for temporally structured signals.

  • plot_07_spectrogram_dss.py: Time-frequency masking with spectrogram-based DSS.

  • plot_08_blind_source_separation.py: Blind source separation and FastICA equivalence.

  • plot_09_custom_bias.py: Defining custom DSS biases.

  • plot_10_benchmarking.py: Efficiency benchmarking against PCA, ICA, and averaging.

  • plot_11_wiener_masking.py: Adaptive Wiener masking for bursty signals.

  • plot_12_joint_dss.py: Joint DSS for multi-dataset repeatability.

Data Requirements#

  • Synthetic sections run directly with no external data.

  • Examples using MNE datasets download and cache them through MNE when needed.

References#

  • Särelä & Valpola (2005). Denoising Source Separation. J. Mach. Learn. Res.

  • de Cheveigné & Simon (2008). Denoising based on spatial filtering. J. Neurosci. Methods.

  • de Cheveigné & Parra (2014). Joint decorrelation. NeuroImage.

Fundamentals of DSS.

Fundamentals of DSS.

Artifact Correction with DSS.

Artifact Correction with DSS.

Denoising Evoked Responses.

Denoising Evoked Responses.

Denoising Rhythms (Spectral DSS).

Denoising Rhythms (Spectral DSS).

Periodic Signals (SSVEP and Quasi-Periodic).

Periodic Signals (SSVEP and Quasi-Periodic).

Temporal DSS: Time-Shift Regression & Smoothness.

Temporal DSS: Time-Shift Regression & Smoothness.

Time-Frequency DSS: Spectrogram Masking.

Time-Frequency DSS: Spectrogram Masking.

Blind Source Separation and ICA Equivalence.

Blind Source Separation and ICA Equivalence.

Custom DSS: Defining Your Own Bias.

Custom DSS: Defining Your Own Bias.

Efficiency Benchmark: DSS vs PCA, ICA, and Averaging.

Efficiency Benchmark: DSS vs PCA, ICA, and Averaging.

Adaptive Wiener Masking for Bursty Signals.

Adaptive Wiener Masking for Bursty Signals.

Joint DSS (Multi-Dataset Repeatability).

Joint DSS (Multi-Dataset Repeatability).