[FieldTrip] Standardization of EEG recordings collected across days in a within-subject analysis

[Nikolaos Vardalakis]

Hello to the community,

To introduce myself, I am a postdoctoral researcher working on
neuropsychiatric disorders and DBS. The datasets we collect include SEEG
recordings in EMU settings, iEEG recordings from fully-implanted devices,
and scalp EEG for longitudinal monitoring of our patients. I'm interested
in the effects of limbic DBS on cognition, which is why I run cognitive
tasks in our patient cohort.

My main concern (and what I would like to know more about from everyone who
has dealt with this) involves the process of pooling recordings from
multiple days in the context of within-subject analyses. My particular case
involves SEEG data, that I preprocess and clean, then epoch, then calculate
time-frequency power. All of these steps are performed separately for each
session. To compare two conditions (A, and B) in a single session, I simply
log the trial-level power spectra through ft_math and perform permutation
tests with custom code (it is really difficult to do it through fieldtrip
for SEEG data!). For visualization, I just apply standard dB normalization
to the raw-power means of condition A and B against their common baseline
and plot the differences.

My sticking point: data collected across two different sessions/days vary
in power, impedance, EEG electrode placement, etc. My question is this: for
a single subject, how do you deal with data standardization prior to
pooling and what tests do you run? I could compute mean baseline-normalized
spectra per session, average those, and end up with averages, but this is
the approach for group-level stats across participants; I would lose
individual trial structure, therefore can't do permutation tests. I could
try parametric tests, assuming approximate normality, but I would have two
samples. I also considered z-transforming all power values per session and
then running ft_appendfreqdata, ending up with z-scored time-frequency
power. This approach seems unsavory because it destroys mean/variance and I
wouldn't know what statistical tests to run on this dataset. What are the
suggestions of the community?

Apologies for a (very) long first message! Looking forward to reading
everyone's two-cents on this; neural data analysis is a form of art after
all and everyone has their own pipelines.

Best,
Nikos
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