19 Channel EEG and LORETA

Skrats

I saw a couple threads on LORETA from a few years ago. I just wanted to see what the community thought a few years later. I am highly interested in the science of DMN (PCC) activity comparing meditation to other cognitive tasks.

I'm looking at two studies as a reference:

First, this study, which used fMRI to capture DMN/PCC deactivation in meditation versus a focused task.
https://pmc.ncbi.nlm.nih.gov/articles/PMC4529365/

Second, this study, which used LORETA to capture DMN deactivation.
https://drfredtravis.com/Papers/final paper DMN and effortless.pdf

Basically I'm trying to replicate the findings of study 1, but as fMRI is prohibitive, I'm looking to use the LORETA approach of study 2.

Does this sort of thing seem plausible to put together with an openbci-based solution?

Thanks very much for any insight anyone can offer.

Skrats

@wjcroft was kind enough to reply via email while I was waiting for forum access. Posting here for everyone's sake:

I don't know that *LORETA based solutions are your best option at this date. Try a search such as:

https://www.google.com/search?q=open+source+eeg+source+localization

I've appended the AI Overview (from that search) at the end of this email. Some of the resulting packages such as EEGLAB and Fieldtrip are based on Matlab. Unless you have academic access, that is expensive. I see they omitted NeuroPype, but if you have academic access, that might be your best bet as it is a real-time, live solution. Many other packages only do static analysis from recordings.

https://www.neuropype.io/

A number of researchers have attempted to construct live neurofeedback systems that downtrain DMN / PCC activity. One I know of is Robert Lawson, you might contact him.

https://www.linkedin.com/in/robert-lawson-2a800319/

Fred Travis at MIU has done excellent work over the years. I get the impression that his paper is doing static analysis from recordings. Not a live training system. Not sure of your goal.

I do recall this Forum post from 2018, it references a paper which quantifies the resolution loss with lower channel counts. 16 channels may be right on the edge of what is 'usable', but not commonly employed as most source localization work uses more channels. As you know many early *LORETA setups used 19 channels.

https://openbci.com/forum/index.php?p=/discussion/1803/source-imaging-minimum-numbers-of-channels

---

AI Overview

Several open-source toolboxes are available for EEG source localization, including NUTMEG, EEGLAB, FieldTrip, and Brainstorm. These toolboxes offer various methods for solving forward and inverse problems, allowing users to analyze EEG data and estimate the location of active neural sources.
Key Features and Options:
NUTMEG:
Offers a GUI-driven interface for data import, preprocessing, source reconstruction, and functional connectivity analysis, along with interactive data visualization.
EEGLAB:
Provides a flexible platform for EEG analysis and offers plugins for various algorithms, including REST for real-time source mapping.
FieldTrip:
A MATLAB-based toolbox with extensive documentation and tutorials, including functions for dipole fitting and source localization.
Brainstorm:
A popular open-source toolbox for M/EEG analysis, including source localization and connectivity analysis.
Other Options:
Cartool, SESAMEEG, NFT, and SPM8 are also mentioned as potential choices.
Important Considerations:
Preprocessing:
EEG data often requires preprocessing to remove artifacts and noise before source localization.
Forward Modeling:
This step involves creating a mathematical model of the head and brain to simulate how electrical activity at the scalp is related to brain sources.
Inverse Problem:
This is the process of estimating the location and strength of brain sources given the EEG data and the forward model.
Dipole Fitting:
A common approach to source localization, where a single dipole is fitted to the EEG data at different locations and orientations.
Sparse Modeling:
An alternative approach to source localization that assumes the brain activity is generated by a small number of sources.
Datasets and Resources:
LOCALIZE-MI Dataset: An open dataset of simultaneous intracerebral stimulation and HD-EEG in humans, which can be used for validating and testing source localization methods.
iElectrodes: An open-source toolbox for localizing electrodes from MR and CT images.

Skrats

Neuropype jumps out as really interesting. I shot a note off to them and will see how that conversation goes.

Skrats

Passing this along from Roberto D. Pascual-Marqui, the developer of LORETA

Without a doubt EEG is very sensitive to changes of state, and if you have the time and resources, I would certainly encourage using EEG to study changes in brain activity localized with loreta.