[Mne_analysis] Noise estimation in EEG

[Matti Hamalainen]

Hi again,

On Nov 24, 2012, at 9:47 AM, octavian lie wrote:

> Dear All,
>  
> Here is a fundamental question that has been answered in a conflicting way, related to EEG spike or ERP analysis.
>  
> When estimating the noice covariance matrix C of an EEG (data matrix nxt, n=no of electrodes, t=time), should one chose the identity matrix (no assumptions of the brain and measurement noise) or the one calculated on baseline pre spike/prestim epoch? Some think that for EEG, as opposed to MEG, estimating noise is not a good idea, whereas others think it should be ok. (this in the contexs of gaussian and noncorrelation assumption about noise).
> Also, if I should calculate the noise covariance matrix, since I am working with average spikes (calculated from a known no of individual, raw, spikes), should I correct in any way the noise covariance matrix by the no of spikes, and if yes, how do I do it?

Forgot one thing. The noise covariance matrix should be scaled down by the number of spikes in the average. This can be accomplished easily by specifying the number of averages when the computed inverse operator decomposition (from mne_do_inverse_operator) is actually applied to the data.

- Matti




---------

Matti Hamalainen, Ph.D.
Athinoula A. Martinos Center for Biomedical Imaging
Massachusetts General Hospital

msh at nmr.mgh.harvard.edu
mhamalainen at partners.org






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