In my last post, I gave a brief introduction to intertrial coherence, which is a measure of how consistent oscillatory phase is across an ensemble of trials.
One thing I mentioned, right at the end, was that ITC is a compound, summary statistic that doesn’t in and of itself exist on a single-trial level. This has lead several people to think about how to link it to single trial behavioural measures such as reaction time.
Intertrial coherence (ITC) is a measure of how consistent oscillatory phase is across an ensemble of trials.
In the schematic below, we have a sine wave of an arbitrary frequency.
The amplitude of the wave is the distance between 0 and the wave at a given timepoint. So here, you can see it has a maximum of 1, indicated by the arrow. A complete cycle of of the wave is the amplitude peaking, declining to a trough, then hitting zero again.
How averaging over repetitions produces an event related potential and separates signal from noise
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.
A few months ago I wrote a post about how there isn’t really a killer EEG analysis package for R, and that many of the things you typically want to do are not really implemented yet. So I’ve started to implement several functions myself and incorporate them into my own package, currently called eegUtils. I’ll maybe come up with a catchier name at some point before I get to the stage of trying to get it on CRAN.