# How choose source localization algorithm for EEG

**URL:** <https://mne.discourse.group/t/how-choose-source-localization-algorithm-for-eeg/10768>\
**Category:** Support & Discussions\
**Created:** [February 5, 2025, 1:32pm UTC](https://mne.discourse.group/t/how-choose-source-localization-algorithm-for-eeg/10768 "2025-02-05T13:32:03Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![FannyGrosselin](https://yyz2.discourse-cdn.com/free1/user_avatar/mne.discourse.group/fannygrosselin/32/4035_2.png) [@FannyGrosselin](https://mne.discourse.group/u/FannyGrosselin)\
**Post date:** [February 5, 2025, 1:32pm UTC](https://mne.discourse.group/t/how-choose-source-localization-algorithm-for-eeg/10768/1 "2025-02-05T13:32:03Z")

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```python
MNE version: 1.7.1
operating system: Windows 10

```

Dear MNE community,

I am studying EEG data and I am particularly interested in theta and beta bands source localization in different conditions and population.

I am using the default method used in mne.minimum\_norm.apply\_inverse\_tfr\_epochs, that is to say dSPM. However, I don’t find any reference paper that advice to use dSPM rather than MNE or beamformer for studying source localization of some frequency bands in EEG.  
Any advice, explainations and/or ref articles would be very appreciated.

Best regards,

Fanny

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<div class="post-metadata">

**Author:** ![SBeumer](https://avatars.discourse-cdn.com/v4/letter/s/f0a364/32.png) [@SBeumer](https://mne.discourse.group/u/SBeumer)\
**Post date:** [February 6, 2025, 12:01pm UTC](https://mne.discourse.group/t/how-choose-source-localization-algorithm-for-eeg/10768/2 "2025-02-06T12:01:54Z")

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I would highly suggest diving into the literature on source localization algorithms to understand the effects of choosing one algorithm over another, for example this summary paper by Cristophe Michel ([Frontiers | EEG Source Imaging: A Practical Review of the Analysis Steps](https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2019.00325/full))

In principle, no algorithm is perfect because there is an infinite number of possible solutions to the inverse problem and by choosing an algorithm, you choose the type of solution obtained. For example the standard minimum norm approach will give you highly spread cortical sources, while a dipole approach will give you very focal sources.