Zapline-plus: a Zapline extension for automatic and adaptive removal of frequency-specific noise artifacts in M/EEG

Abstract

Removing power line and other frequency-specific artifacts from electrophysiological data without affecting neural signals remains a challenging task. Recently, an approach was introduced that combines spectral and spatial filtering to effectively remove line noise: Zapline (de Cheveigné, 2020). This algorithm, however, requires manual selection of the noise frequency and the number of spatial components to remove during spatial filtering. Moreover, it assumes that noise frequency and spatial topography are stationary over time, which is often not warranted. To overcome these issues, we introduce Zapline-plus, which allows adaptive and automatic removal of frequency-specific noise artifacts from M/EEG data. To achieve this, our extension first searches for peaks in the spectrum, then applies Zapline iteratively to chunks of data to account for non-stationarity of the noise over time. Likewise, the exact noise frequency around the found target frequency is determined separately for every chunk to allow fluctuations of the peak noise frequency. The number of to-be-removed components is automatically determined using an outlier detection algorithm. Finally, the frequency spectrum after cleaning is analyzed for sub-optimal cleaning and parameters are adapted accordingly before re-running the process if necessary. The software creates a detailed plot for analysis of the cleaning that can be used to inform manual changes. We showcase the efficacy of the different features of our algorithm by applying it to four openly available data sets, two EEG sets containing both stationary and mobile conditions, and two MEG sets.

No need for extensive artifact rejection for ICA - A multi-study evaluation on stationary and mobile EEG datasets

“…The Auditory Gait data set had a notch filter applied before uploading. All other data sets were processed with Zapline-plus (de Cheveigné, 2020; Klug & Kloosterman, 2022) to remove line noise and other frequency-specific noise peaks. Zapline (de Cheveigné, 2020) removes noise by splitting the data into an originally clean (data A) and a noisy part (data B) by filtering the data once with a notch filter (A) and once with the inverse of this notch filter (B).…”

Mobile electroencephalography captures differences of walking over even and uneven terrain but not of single and dual-task gait

“…Artifact subspace reconstruction ( 53 ), was calibrated with a standing baseline at the beginning of the recordings and used to correct artifacts with a cut-off of SD = 20, following the version's default settings and recommendations ( 54 ). Then line noise was corrected with the zapline plus tool ( 55 ) (available at https://github.com/MariusKlug/zapline-plus , retrieved 26.10.21, with noise frequency 50 Hz, highest Frequency 61 Hz) an extension to zapline ( 56 ). After spherical interpolation of the previously rejected channels, channels were re-referenced to the full rank common average (version 0.10, available at http://sccn.ucsd.edu/eeglab/plugins/fullRankAveRef0.10.zip ).…”

The BeMoBIL Pipeline for automated analyses of multimodal mobile brain and body imaging data

“…However, parameters can be adjusted if the cleaning does not work as intended. See (Klug & Kloosterman, 2022) for details about the processing and parameter tweaking. This step can be avoided by setting the whole bemobil_config.zaplineConfig field to empty.…”

“…Frequency-specific noise is removed with Zapline-plus (Klug & Kloosterman, 2022). Zapline-plus is an EEGLAB plugin that removes spectral artifact peaks automatically.…”