Dear SPM team,
I am working with intraoperative subthalamic nucleus (STN) microelectrode recordings obtained from parkinsonian patients. Using Moran's (2008, in 'Brain') background extraction algorithm, which removes spikes from a spike train, I am working with (spikeless) background activity signals and I have averaged my epoched signals using SPM relative to a total of 9 triggers. I have signals from 12 patients, with some amplitude variability, so I am concerned that the frequency content in those with the higher amplitudes will dominate the grand averages over those from patients whose signals have lower amplitudes. Can you tell me (or point me to the literature regarding) how the M/EEG SPM suite normalizes when grand averaging? Are the grand averages first performed on the raw signals or rather on normalized TF data? Here are the functions I'm using:
spm_eeg_epochs(S)
spm_eeg_ft_multitaper_tf(S)
spm_eeg_average_TF(S)
spm_eeg_tf_rescale(S)
spm_eeg_grandmean(S)
Best regards,
Uri Ramirez, PhD candidate
Neurologische Klinik
Würzburg, Germany
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