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Hi Nikos,
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<div>Perhaps starting from this paper may be helpful <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1016%2Fj.neuroimage.2022.119438&data=05%7C02%7Cfieldtrip%40science.ru.nl%7C126e68aaff2f45d2a4f608de2dd941cc%7C084578d9400d4a5aa7c7e76ca47af400%7C1%7C0%7C638998608281656566%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=kcpL2Ix881elEjE6yAmM8ltZGKgVaNICTPta0vWs9qw%3D&reserved=0" originalsrc="https://doi.org/10.1016/j.neuroimage.2022.119438">https://doi.org/10.1016/j.neuroimage.2022.119438</a> The
relevant sections may contain references to literature that you may want to consult.</div>
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<div>Also, in general, you probably would need to first be explicit on whether you want to run statistical inference across participants (using a statistical test with participants as unit-of-observation) , or per participant (using a statistical (not necessarily
permutation) test with trials as unit-of-observation). With sEEG recordings I would say there is much more to worry about that across session fluctuations in SNR - which I (naively) would probably address by zscoring the time domain signals prior to spectral
transformation.</div>
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<div>Also, please feel free to suggest new or amend existing online documentation to reduce the non-intuitiveness of running permutation tests with cluster-based correction for multiple comparison in situations where the spatial dimension does not lend itself
for clustering…</div>
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<div>Best wishes,</div>
<div>Jan-Mathijs</div>
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<div>On 27 Nov 2025, at 16:59, Nikolaos Vardalakis via fieldtrip <fieldtrip@science.ru.nl> wrote:</div>
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<div dir="ltr">Hello to the community,
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<div>Apologies if cross-posting, I might have messed up the first time I shared my question. 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 (arguably very small) patient cohort.</div>
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<div>My main concern (and what I would like to know more about from everyone who has dealt with this) involves the pooling of single-patient recordings from multiple days. My particular case involves SEEG data that I preprocess, epoch, and calculate time-frequency
power per trial. All of these steps are performed separately for each session. To compare statistically two conditions (A vs 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 unintuitive how to do it using ft_freqstatistics for SEEG data with no neighbors!). For visualization, I just apply standard dB normalization to the raw-power means of condition A and B against their common baseline and plot their difference map.</div>
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<div>My sticking point: data collected across two different sessions/days vary in power, impedance, signal quality etc. My question is this: for a single subject, how do you deal with data standardization prior to pooling and what statistical tests do you run?
I could compute mean baseline-normalized spectra per session, average those, and end up with patient/condition averages, but this is the approach for running a group-level statistical analysis across participants; I would lose individual trial structure, therefore
can't do permutation tests. I also considered z-transforming all power values per session and then running ft_appendfreqdata, ending up with z-scored time-frequency power per trial, and then pool all trials from each session together. 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?</div>
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<div>Apologies for a (very) long first/second 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.</div>
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<div>Best,</div>
<div>Nikos</div>
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