Seeking advices for baseline correction in SEEG time-frequency analysis

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Hi Kun,

I haven’t worked with SEEG, but I have with MEG. Here’s my advice:

  1. I would not recommend using apply_baseline(baseline=(None, 0), mode=??) to the trial-level TFR. (see: General question: Applying baseline). I believe it’s best not to over-clean the data, as we risk losing valuable signals. I’d only clean them if there were clear artifacts. However, keep in mind that opinions vary and some believe baseline correction can increase statistical sensitivity.

  2. I suggest using mode=‘ratio’ or ‘zratio’ instead of ‘mean.’ This makes data unitless, and easy to compare between modalities like MEG and EEG, Also, it deals better with the brain’s 1/f effect (see: [FieldTrip] TFR Baseline Correction Type).

  3. If you apply baseline correction to the trial-level TFR, I recommend applying it before cropping trials (see: Question about baseline in time-frequency representation - #3 by drammock).

  4. During epoching of your data with mne.Epoch, I think you can’t specify a mode. I think it automatically subtracts the mean amplitude of the pre-stimulus EEG signal, as you already pointed out.

I hope that helps! :slight_smile:

Best,
Konstantinos