[Eeglablist] General Continuous Data Analysis Questions

[Evan Wilson]

Dear List,

I am a novice in need of some general guidance.  I am trying to meet a deadline for my research project (next week) and I am really struggling with data analysis.  My professors are completely unfamiliar with EEGLAB so my knowledge only encompasses what I could gather from tutorials.

I currently have a raw .edf file (I used the Emotiv EPOC research edition).  I am trying to examine frontal alpha asymmetry at specific points in an experience and compare it across two separate conditions (one where subjects were walking around so I assume there are quite a lot of artifacts to remove).  These are the steps I know how to do so far:

1. I imported the continuous data

2. I added channel locations (although EEGLAB gives me trouble, saying there can’t be empty channels, but I found a way around this)

3. Remove the baseline (I was told this is likely unnecessary)

4. Run ICA (do I do this before or after adding high and low pass filters?)
- Also, what does this do as far as filtering the data? I am still a little confused about this but I am under the impression that it is a pivotal step.

5. It was suggested that I could then just filter the data down to ~7 - 14 Hz and this will filter out most artifacts and preserve the alpha bands, but this seems fishy to me.

6. Epoch the data to just the three or four instances I wish to examine
-I am unsure about this part.  I do not know if this is the correct next step nor do i know how to epoch the data, but I figure I can refer back to the tutorials for the latter.

7.  Spectral analysis of the epochs to then compare frontal alpha asymmetry
-How would I calculate a significant difference?

Please let me know what I am missing/what else you think I should be doing in order to efficiently get valid data that I can start comparing and writing about.  Sorry if these are really basic questions, guys, but I am looking down the barrel right now and nobody else has been able to help me so far.

All the best,

Evan