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 often considered to be mere "noise".
The problem is that these filters have side effects: as well as 'cleaning up' the data, they can also distort it. There are two main kinds of filter: causal filters are well-known to mutate the signal. Acausal high-pass filters avoid these dramatic artefacts -
But David Acunzo and colleagues point out that acausal filters can actually be more dangerous, because they still distort the data, just in more subtle ways that are harder to spot. In particular, acausal filters can alter the signal at time points before the true signal begins. See the pic above.
That's not necessarily a problem in all cases, but it's certainly bad news for researchers interested in measuring exactly when neural responses happen.
The authors highlight an area of neuroscience where this problem could be misleading researchers. The very earliest brain responses to visual stimuli, about 90 milliseconds after the stimulus onset, is called the "C1" response. Classically, it was thought that the size of the C1 wave was purely a 'bottom-up' phenomenon, determined only by the brightness etc. of the stimulus. But recently, studies have reported 'top down' modulation of C1 by attention, emotional state, etc.
Acunzo et al point out that many of these studies used strong acausal filtering and that what might be happening is that attention actually causes late changes to the visual response, but that due to filtering artefacts, these late changes appear in the data sooner than they really happen. They advise that only weak (low threshold) high-pass filters should be used, and that interesting findings in filtered signals need to be checked against the raw data.
Acunzo DJ, Mackenzie G, and van Rossum MC (2012). Systematic biases in early ERP and ERF components as a result of high-pass filtering. Journal of neuroscience methods PMID: 22743800