# How do I obtain recordings of the P300 wave of the event-related potential in the EEG?

I am trying to understand how to analyze the ERP (Event-Related Potentials) from EEG recordings and in particular the P300 wave.

I have come up with a few questions which I hope you might be able to help with:

1. I assume that the number of repetitions of the stimulus should give better results, but I am not sure why. On the one hand, it should help, because averaged results are usually better in electrophysiology. On the other hand, maybe there is some unwanted adaptation effect in the subject? So, are repetitions wanted in this kind of experiment?

2. There are time points in which I didn't expect P300 but the brain activity was not 0. I understand that there is always a background activity in the brain, but maybe there is more to it? And if so, are there certain frequencies that are expected in the EEG?

3. When I analyze the data using an average response, should I assume anything on the P300 response?

4. What is the recommended sample Rate I should use? I guess there should be an advantage for high sample rates to increase resolution, but I am not sure about that. Am I right, or maybe a lower sample rate has advantages that I am not aware of?

• Read an introductory textbook, such as Luck's book on event-related potentials. I'm voting to close as this is much too broad. – jona Apr 7 '16 at 13:34
• This paper has some good info (but the Luck book is better): ncbi.nlm.nih.gov/pmc/articles/PMC3816929 . Question 1 is an interesting question. 2-3 don't make sense. Question 4: sample rate should not make a difference for a simple P300 ERP analysis. The only benefit to you of a lower sample rate will be smaller files and less computational overhead. – K A Apr 13 '16 at 21:45

• ad. 1: Averaging reduces the random background EEG activity (noise), as well as artifacts due to movement or eye blinks. Random events will average out, while the ERP itself, being highly synchronized with the stimulus, will persist in the signal. In short: averaging increases the signal-to-noise ratio (SNR). Is this wanted? - That depends on the SNR you wish to obtain. The more averages, the better the SNR. [M]aybe there is an unwanted adaptation effect of the subject - if you are afraid of adaptation - do repeated measures and analyze single ERPs, or small chunks of averages across the recording - it will give you an idea whether there is adaptation, and if yes, how much. I wouldn't worry about it too much. The long time scale of P300 recordings (second-range) allows the nervous system plenty of time to recover.
• ad. 2. There is always background EEG, except in (brain) dead subjects - and these subjects won't be too cooperative. [A]re there certain frequencies that are expected in the EEG - ERPs are not analyzed in the frequency domain, but in the time domain. If you are interested in the frequency content of the EEG; it is much dependent on the state of the subject. An awake, vigilant test subject with their eyes open will show beta activity (6 - 31 Hz band).