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When Data Filtering Introduces Bias

Explore how high-pass filtering impacts neural responses, revealing biases in the C1 response and implications for ERP studies.

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Oh no. Anotherworrying methods problem for neuroscience, this time for electrophysiologists:

Systematic biases in early ERP and ERF components as a result of high-pass filtering.

The event-related potential (ERP) and event-related field (ERF) techniques provide valuable insights into the time course of processes in the brain. Researchers commonly filter the data to increase the signal-to-noise ratio. However, filtering may distort the data, leading to false results. Using our own EEG data, we show that acausal high-pass filtering can generate a systematic bias easily leading to misinterpretations of neural activity... among 185 relevant ERP/ERF publications, 80 used cutoffs above 0.1Hz. As a consequence, part of the ERP/ERF literature may need to be re-analyzed.

The problem in brief: many researchers use a high-pass filter on their electroencephalography (EEG) and magnetoencephalography (MEG) recordings of brain electrical activity. A high-pass filter removes low frequency (i.e. slow) changes from the signal. These slow fluctuations are ...

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