r/compmathneuro

If you had seen my previous post of peak spread: https://www.reddit.com/r/compmathneuro/comments/1szlrgy/preliminary_peak_spread_montage_from_processed/, I have further developed these renderings to use 8 specific bands with an overall view as well now, using differential entropy standard deviation this time instead.

This rendering uses 5 second intervals of a processed seizure recording broken down into 3 phases: preictal (up to 20 minutes before seizure), ictal or seizure period, and then preictal period until the end of the recording. Here are the bands with the "infraslow" band being the only band that wasn't used:

BANDS = {
    'overall':    (0.1, 100.0*),
    'infraslow':  (0.1,   0.5),
    'delta':      (0.5,   3.5),
    'theta':      (3.5,   8.0),
    'alpha':      (8.0,  13.0),
    'low_beta':   (13.0, 20.0),
    'high_beta':  (20.0, 30.0),
    'gamma':      (30.0, 50.0),
    'high_gamma': (50.0, 80.0),
    'ripples':    (80.0, 100.0*),
}

* is a placeholder, the processor determines the highest frequency and then sets to that.

The videos are then rendered at 30 fps with added transition frames for smoothing.

For each band and EEG channel that is then mapped to a DK region, variance is computed across the 5 second time samples using the closed-form differential entropy of a Guassian distribution, with a small epsilon added to prevent a log of zero: 0.5 · log(2πe·σ²).

Although I am still trying to understand what this may be telling me, I think it is very interesting to see some patterns emerging, especially in the superiorparietal area. As I understand it, this is spatial variability across channels/regions showing coherence in blue and noisy/high-variance in red. I am a broad data scientist with a background in stats who took interest in EEG recordings and seizures, so I am still trying to learn a lot more about the medical side of things.

Anywho, hope you like it!

Note this has not been peer-reviewed or clinically proven.

github: https://github.com/amathislab/musclemimic

MuscleMimic is a JAX-based motion imitation learning research benchmark specifically designed for biomechanically accurate muscle-actuated models. It focuses on advancing research in muscle-driven locomotion and manipulation through high-performance neural policy training. 

Link for online interactive demo:
https://pixedar.github.io/ai/mindvisualizer/

Main GitHub repo:
https://github.com/Pixedar/MindVisualizer

This is a follow-up to my open-source brain information flow exploration repo from this post:

https://www.reddit.com/r/compmathneuro/comments/1sy150g/open_source_brain_information_flow_exploration

I decided to make a small online demo of the repo to make the idea more accessible to a broader group of people, and to give people an easier way to first interact with the visualization.

I see the web demo mostly as an entry point into the broader effort and repo. More broadly, I see this as part of a larger effort to build better intuition and mental models for large-scale brain dynamics. I know the current technology and methods may not be fully there yet, but I think this kind of exploratory / collaborative tooling is emerging and worth trying

However, a few caveats:

• The current flow data is not peer-reviewed. It is based on real brain data from my preprint / Zenodo record: https://zenodo.org/records/18200415 In the future, it would be nice to turn this into a more rigorous version, possibly with higher-quality data, better-validated flow models, or collaboration with people who work more directly on this kind of problem.
• Please remember that the online demo is only a limited demo. It currently shows only one of the three modes from the full repo. The other modes in the repo may actually be more important / relevant than the one currently shown in the browser demo, especially for the broader brain-manifold and information-propagation idea. For the full functionality, please check the actual GitHub repo: https://github.com/Pixedar/MindVisualizer
• The real repo is the main project, not the web demo. It contains the three modes, the broader brain-manifold / information-propagation idea, the LLM/RAG interpretation part, and the informal observations file: https://github.com/Pixedar/MindVisualizer/blob/master/OBSERVATIONS.md The observations file is there so people can add interesting flow paths, perturbation effects, or intuitions about resting-state organization. The hope is to slowly build a shared record of patterns that might help us think about how the brain works internally.
• The site is intended for demo / accessibility purposes only. The web version was made more quickly just to make the idea easier to try in the browser. The GitHub repo is the more complete version of the project, with more functionality and better code structure. For anything beyond just trying the browser demo, please look at the repo.
• I do not expect a huge amount of traffic, but since the LLM analysis costs tokens, I included only a small amount of my own credits, so it may run out over time if people use it.

The original repo post was basically about combining brain information flow derived from real fMRI and tractography data with an LLM, including RAG-based interpretation of this flow and propagation of information in the brain.

It is still not peer-review quality and should rather be treated as a tool for building intuition about the brain and building a mental model of brain dynamics.

Feedback is very welcome, especially from people who know the field better or have ideas about validation, better data, better flow models, or how to make the observation/collaboration part more useful