ZapLine Examples#
Overview#
Examples demonstrating ZapLine and ZapLine-plus for removing power-line artifacts from synthetic, epoched, continuous, and adaptive-cleaning scenarios.
Files#
plot_01_basic_usage.py: Basic ZapLine usage on synthetic line-noise data.plot_02_parameter_tuning.py: Parameter tuning and real NoiseTools MEG data.plot_03_epoched_data.py: Epoched ZapLine workflows and high-channel MEG data.plot_04_adaptive_mode.py: ZapLine-plus style adaptive cleaning on non-stationary data.plot_05_adaptive_advanced.py: Advanced harmonic and chunk-level adaptive outputs.
Data Requirements#
Synthetic sections run directly with no external data.
Examples using MNE datasets download and cache them through MNE when needed.
NoiseTools-backed examples download and cache the required .mat files into
examples/zapline/datathe first time they are run.
References#
de Cheveigné (2020). ZapLine: A simple and effective method to remove power line artifacts. NeuroImage.
Klug & Kloosterman (2022). Zapline-plus: A Zapline extension for automatic and adaptive removal of frequency-specific noise artifacts in M/EEG. Human Brain Mapping.
ZapLine-plus: Adaptive Cleaning on Non-Stationary Noise.
