What are the steps to detect ErrP (Error related potentials) in EEG signals?

Question

I have EEG recordings of an experiment. In some of these recordings subject made an error during the experiment. We know these error cause brain to output ErrP signals. I want to detect this. There are not many sources about single trial detection of ErrP's. What are the main steps to detect these error indicator signals?

Error-related potentials (ErrPs) are neurophysiological signals associated with error processing. They are generated when wrong actions are perceived and have been reported in many contexts in the past two decades, namely when a subject perceives that he/she has committed an error and recognizes it immediately (‘response ErrP’), when a subject receives the feedback of a previous choice without knowing whether it was wrong (‘feedback ErrP’), when observing mistakes of another person or agent (‘observation ErrP’)’ or during the interaction with a brain-computer interface (BCI) when the feedback is not the expected one (‘interaction ErrP’). The components of an ErrP appear within a time window of 500 ms and are naturally elicited in the brain without the user’s explicit intention. Thus, its automatic detection can be used in myriad ways, in real-time, and in human-machine interaction processes. In particular, interaction and observation ErrPs have been applied as a proof-of-concept in several applications, for example, for detection and correction of BCI choices to increase reliability, to adapt BCI systems over the time, or to make intelligent systems (e.g., external agents) learn. There is also a growing interest for ErrPs in clinical applications for disorders where error monitoring is impaired.

Answer 1

An "error related potential" is just a difference in EEG traces between error and non-error trials.

To calculate an (anything)-related potential, you average trials with and without (anything), and subtract one from the other. It's possible to do this in time or frequency domains, though you'll likely get different answers due to evoked vs induced components (see David, O., Kilner, J. M., & Friston, K. J. (2006). Mechanisms of evoked and induced responses in MEG/EEG. Neuroimage, 31(4), 1580-1591. ). If you want to isolate errors specifically, you'll want to match trials where other things are constant. For example, if your subjects are choosing left or right, you may want to subtract trials where they chose left as a correct option from those where they chose left as an error. The exact way of doing this depends entirely on your experimental paradigm. Read the literature for ideas, but the specifics depend on your specifics.

Since you know which trials are errors, you do not need to detect errors on single trials to demonstrate if some error signal is present.

If you want to do single-trial detection, that will be a matter of finding the features in the average error signal in a single trial or identifying non-phase locked features (such as power changes) that don't show up well in averages. You might look for power in particular frequencies, voltage at a specific moment in time relative to a stimulus/decision, phase relationships on different channels, etc. You could train a neural network (or use other AI/ML approaches) to detect these features using standard approaches in those fields (cross-validation, etc).

Single-trial detection with EEG is very difficult. I suspect you will fail to distinguish on single trials from EEG with high accuracy despite what you've read, but you're welcome to try, and can probably exceed chance performance if the quality of recordings and strength of signal are good.

Here are a few papers where people have done similar things:

Chavarriaga, R., & Millán, J. D. R. (2010). Learning from EEG error-related potentials in noninvasive brain-computer interfaces. IEEE transactions on neural systems and rehabilitation engineering, 18(4), 381-388.

Ferracuti, F., Casadei, V., Marcantoni, I., Iarlori, S., Burattini, L., Monteriù, A., & Porcaro, C. (2020). A functional source separation algorithm to enhance error-related potentials monitoring in noninvasive brain-computer interface. Computer Methods and Programs in Biomedicine, 191, 105419.

Salazar-Gomez, A. F., DelPreto, J., Gil, S., Guenther, F. H., & Rus, D. (2017, May). Correcting robot mistakes in real time using EEG signals. In 2017 IEEE international conference on robotics and automation (ICRA) (pp. 6570-6577). IEEE.

None of their approaches are identical; there is no "right" answer here.