I am working on learning with EEG data in young and elderly subjects. The goal is to find differences in P300 (amplitude & latency) between both age groups during learning. I analyzed the data based on Grand Averages of both groups. As a confirmatory analysis I've tried to find the P300 peak for each subject individually and I performed analysis based on this peaks. Now I am interested in the variability in P300 latency in each trial, especially in elderly subjects, as the decreased P300 amplitude might be partially explained by averaging of more variable peaks. The single trial data looks very noisy and based on the fixed time window for searching P300 peaks, I am getting very different latencies for each trial. The topographies of single trial data also does not help - some looks ok, but others are just noise. Does someone know, how to handle P300 in single trial EEG data? Are classification methods for BCI going to work for this project? Best, Dawid

I am learning to work with EEG data in young and elderly subjects. The goal is to find differences in P300 (amplitude & latency) between both age groups during learning. I analyzed the data based on Grand Averages of both groups. As a confirmatory analysis I've tried to find the P300 peak for each subject individually and I performed analysis based on this peaks. Now I am interested in the variability in P300 latency in each trial, especially in elderly subjects, as the decreased P300 amplitude might be partially explained by averaging of more variable peaks. The single trial data looks very noisy and based on the fixed time window for searching P300 peaks, I am getting very different latencies for each trial. The topographies of single trial data also does not help - some looks ok, but others are just noise. Does someone know, how to handle P300 in single trial EEG data? Are classification methods for BCI going to work for this project?