Time-frequency Transform

Matt Craddock's blog about cognitive neuroscience, psychology, and statistics

EEG processing with Python, but in R: redux

Two years ago I wrote a post demonstrating Python pre-processing of EEG data using Python chunks in an RMarkdown document. This worked great. But something I mentioned then was that there was a package called reticulate that would allow more direct interfacing with Python in R. That package has been under a lot of development since then, as has RStudio. Plots produced in Python chunks can now be embedded in RMarkdown.

Building up an ERP

Event-related potentials are one of the simplest ways of representing event-locked EEG data. Imagine a very simple visual experiment in which participants have to respond to pictures of objects. You set up the experiment so that an event trigger is sent to your EEG recording system at the time the picture flashes up screen. Then, afterwards, you extract epochs around those triggers to get a bunch of seperate epochs, each time-locked to the onset of the stimulus.

More on reaction times

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.

Analysing reaction times - Revisiting Ratcliff (1993) (pt 1)

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.

Loading EEGLAB .set files in R - part 2

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.