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

[Schoffelen, J.M. (Jan Mathijs)]

Hi Nikos,

Perhaps starting from this paper may be helpful https://doi.org/10.1016/j.neuroimage.2022.119438<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> The relevant sections may contain references to literature that you may want to consult.

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.

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…

Best wishes,
Jan-Mathijs


On 27 Nov 2025, at 16:59, Nikolaos Vardalakis via fieldtrip <fieldtrip at science.ru.nl> wrote:

Hello to the community,

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.

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.

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?

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.

Best,
Nikos

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