Hi everyone,
Great to be here and meet everyone. This is my first post and as the title suggests, I am seeking advice on standardizing EEG recordings collected from the same participant but across multiple days/sessions. A few words about the experimental design:
I am dealing with a patient cohort that is pretty rare, which makes data availability pretty tough. Patients are tested on a set of cognitive tasks, longitudinally across days. And this detail makes things complicated…
As a first step, I am computing time-frequency spectra and I am trying to assess if condition A is significantly different from condition B. Any significant differences will then be source-localized using beamforming methods. As this is a within-subject design, for argument’s sake let’s assume there are 4 recordings in total and each recording contains both conditions. I am trying to find an appropriate way to standardize the data, so that I can perform statistical analyses.
I am really unsure how to standardize my data prior to pooling and then testing condition A vs condition B. In a group-level design, I would compute session-specific dB-normalized maps, then average those, get a subject-specific time-frequency map, and then be on my way to SPSS. However, for a within-subject design, things get… complicated.
How do you approach such issues in within-subject designs? Is there an “ideal” way to standardize data prior to pooling and hypothesis testing?
Thank you a bunch for your input!
