On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP

By Irene Winkler, Stefan Debener, K. Müller, Michael W. Tangermann. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2015

380 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/114bb1b3c0af682aae93dcb9a94e44c31a352ee4

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Citations in the corpus

The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data (2018, methodology): “The ﬁlter removes non-stationary signal drift across the recording and serves as a pre-processing step for the electrical noise removal and ICA steps that follow (Bigdely-Shamlo et al., 2015; Winkler et al., 2015).”
Analysing concurrent transcranial magnetic stimulation and electroencephalographic data: A review and introduction to the open‐source TESA software (2017, positive): “However, filtering can also improve the performance of ICA, increasing component dipolarity, signal to noise ratio, and neural classification using an automatic classifier (Winkler et al., 2015).”
Hemispherotomy leads to persistent sleep-like slow waves in the isolated cortex of awake humans (2025, unknown): “…weights (estimated from the data filtered with a 0.5 Hz high-pass) were applied to the original data (i.e., that filtered with a 0.1 Hz high-pass), and the retained components were back-projected to the scalp, thus effectively removing artifactual influences from the scalp electrodes signal [73].”
Longitudinal trajectories of aperiodic EEG activity in early to middle childhood (2024, methodology): “Data were resampled to 250Hz, and then preprocessed using the HAPPE module, which includes line noise removal, bad channel rejection, and artifact removal using combined wavelet-enhanced independent component analysis (ICA) and Multiple Artifact Rejection Algorithm (MARA; (Winkler et al., 2015).”
The developmental trajectory of functional excitation‐inhibition balance relates to language abilities in autistic and allistic children (2023, result): “These rejection rates are comparable to manual validation approaches in developmental (Gabard-Durnam et al., 2018) and adult data (Winkler et al., 2011; Winkler et al., 2015).”