ERP Data Processing: Recommendations

How you process your data prior to analysis will affect the power of your mass univariate analyses. In the case of approximate permutation tests, it will also effect the Type I error rate (since noisier data will have a higher Type I error rate).

Of course, the decisions you make about how to process your data are important no matter how you analyze your data. Here I mention only two such decisions, because they have particular importance for mass univariate analyses.

Sampling rate

A higher sampling rate means more data points and thus a potentially larger multiple comparisons problem. How this affects mass univariate analysis depends on which correction you use.

The cluster mass approach and FDR approach will probably not be significantly affected by a higher sampling rate. FDR correction depends on the proportion of significant effects (i.e., significant electrodes/time points divided by total electrodes/time points). This isn't likely to change much with sampling rate. The sampling rate probably will also not significantly affect the cluster mass test: if two adjacent time points at a sampling rate of 250 Hz are in a cluster, the time point between them at 500 Hz will probably also be included. Clusters will be larger, but the rankings aren't likely to change.

The Fmax approach is affected by sampling rate. The maximum F for each permutation will grow with increasing sampling rate, therefore decreasing power. If you plan to use the Fmax approach, you should downsample your data. Something around 100 Hz will provide enough temporal resolution in most cases.

Note that this has nothing to do with the sampling rate when you collect data. You can downsample from the original sampling rate either in your data processing software or with the MUT function decimateGND.

Whatever approach you use, a lower sampling rate will mean your anlayses will run faster. For complex designs with a large number of permutations, downsampling may be worth it even if you are using the cluster mass or FDR tests.

For further discussion, see the Mass Univariate Toolbox documentation, Groppe et al. (2011a), and Luck (2014).

High frequency noise and filtering

Because individual time points are examined, mass univariate analyses are particularly affect by high frequency noise.

If you plan to use mass univariate analyses, you should attempt to minimize high frequency noise as much as possible (e.g., use jittered presentation of stimuli, eliminate sources of electrical noise).

You should also apply a low pass filter to your data before analysis to remove high frequency noise. Generally a lower cutoff for your low pass filter will increase power, but make sure you understand the effects of the filter you are applying: filters can distort data and have non-intuitive effects. For an excellent introduction to filtering, I recommend Steve Luck's An Introduction to the Event-Related Potential Technique. You should especially be careful about low pass filters when temporal resolution is important or when component overlap is a concern (since filters spread information out in time).