# Trying to understand the lambda2 parameter in mne.minimum\_norm.apply\_inverse\_raw

**URL:** <https://mne.discourse.group/t/trying-to-understand-the-lambda2-parameter-in-mne-minimum-norm-apply-inverse-raw/6764>\
**Category:** Support & Discussions\
**Tags:** source-localization, eeg\
**Created:** [April 20, 2023, 5:49am UTC](https://mne.discourse.group/t/trying-to-understand-the-lambda2-parameter-in-mne-minimum-norm-apply-inverse-raw/6764 "2023-04-20T05:49:31Z")\
**Posts on this page:** 1\
**Showing post:** 6

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**Author:** ![neurothew](https://yyz2.discourse-cdn.com/free1/user_avatar/mne.discourse.group/neurothew/32/682_2.png) [@neurothew](https://mne.discourse.group/u/neurothew)\
**Post date:** [April 20, 2023, 5:01pm UTC](https://mne.discourse.group/t/trying-to-understand-the-lambda2-parameter-in-mne-minimum-norm-apply-inverse-raw/6764/6 "2023-04-20T17:01:21Z")

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@drammock yea I also noticed this function. I think that it is suitable for MEG which can collect empty room data, or for ERP analysis as there is usually a pre-stimulus baseline. Do you think it is suitable for resting-state EEG too?

I am hesitating as I notice that there is a thread [noise covariance matrix in resting state eeg data - 💬 Support & Discussions - MNE Forum](https://mne.discourse.group/t/noise-covariance-matrix-in-resting-state-eeg-data/5788) which the `make_ad_hoc_cov` was recommended.

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