I previously replicated some simulations from a journal article by Ratcliff (1993) 1. These sims demonstrate that transformations such as taking inverse RTs typically improve power to detect RT differences across conditions. The problem is that the mean alone is a poor estimate of the central tendency of the underlying RT distribution, particularly in the presence of outliers - see the previous post for details.
Reaction times are a very common outcome measure in psychological science. Frequently, people use the mean to summarise reaction time distributions and compares means across conditions using ANOVAs. For example, in a typical experiment, researchers might record reaction times to familiar and unfamiliar faces, and look for differences in mean reaction time across these two types of stimuli. An issue with this is that reaction time distributions are skewed: there are many more short values than long values, so their distribution has a long right tail.
In the last post, I showed how you can get the EEG data from EEGLAB .set files saved as Matlab v7.3 files, but that there are some limitations on what else you can get from them beyond the data itself. Specifically, you can’t extract channel locations, and there are no labels to tell you which channels the data is from. This is due a limitation of the available tools for reading HDF5 files, which is the actual format of Matlab v7.
Like a lot of people, I’ve been using EEGLAB and Fieldtrip for years and have a lot of data already processed using those packages. It can be a bit annoying getting the data from them - in the past I’ve converted the data to text/csv files, which is ok as far as it goes. It’s a bit of a faff getting them in the right format, and EEGLAB’s in-built export function drops useful info like epoch numbers and event codes etc.
As mentioned in my last post, I’ve been working on a package for EEG analysis in R called eegUtils. I’d mostly been focusing on relatively simple visualization tools: topographical plots - ERP Visualization: Creating topographical scalp maps: part 1. But one thing was really bugging me - how the data gets into R in the first place. Sure, it’s nice pre-processing data in other packages - EEGLAB or MNE-Python - and then transferring the processed data across to R.