htm.core VS brainflow

OK I am still parsing the pre-print, but their approach seems very interesting. And equally interesting would be to see what happens if you apply HTM [1] to the same inputs model, _e.g._:

> We next applied our paradigm to a morphologically and electrically complex detailed biophysical compartmental model of a 3D reconstructed layer 5 cortical pyramidal cell (L5PC) from rat somatosensory cortex (Fig. 2A). The model is equipped with complex nonlinear membrane properties, a somatic spike generation mechanism and an excitable apical nexus capable of generating calcium spikes

[1] https://github.com/htm-community/htm.core

The home project I've been working on for 4 years (https://github.com/brainflow-dev/brainflow) has been downloaded 100k times from PyPI. It's a library to work with wearable devices, with primary focus on EEG.

About my current user base, I'd like to highlight we use SimpleBLE at my job, it has been integrated into BrainFlow, it is part of some new tools made by a large industrial automation company (whose name I cannot mention but rhymes with "Good product made by bees") and by a robot researcher in Germany.

BrainFlow provides a uniform SDK to work with biosensors with a primary focus on neurointerfaces. It provides SDK for Python, Java, C#, C++, Matlab, R, Julia and Rust. Core part of BrainFlow is written in C\C++ and all bindings call methods from dynamic library. User API is uniform for all supported devices and allows you to develop device agnostic applications.

I made a mod for Skyrim VR that brings your mental focus level (as determined by a Muse 2 meditation EEG device and the processing of https://brainflow.org) into Skyrim and scales your ingame magic power by your actual mental power. So you now actually need to learn to channel your inner strength if you want to be a successful mage! This is Real Virtual Magic!

I can get the complete EEG reading of four channels plus the gyro sensors and in the future also the heart rate. What I do with it is a different question :D I process the data myself, locally, using the brainflow.org library. So I do create a non-binary value at the moment

Sure! I use brainflow.org to collect the data from the muse and at the moment I just do a super simple spectral analysis and use the average alpha/theta ratio to determine brain power. This will definitely not stay. I am already experimenting with different ratios and I will probably implement a classifier that allows a more sophisticated classification later down the road. I will also augment it with the gyro sensor and the heart rate monitor that is integrated in the Muse. Ultimately, I will make it compatible with the OpenBCI Galea device. Join the discord to stay in touch!