Low-cost brain computer interface for everyday use
researchgate.net
researchgate.net
btw, it looks like there is an error with the cert for ironbci.com ? Edit: uh, I guess my phone just didn’t like that it was over http, nevermind
here is open access for paper
Not accessible without login.
Please publish your work on an actual open access site.
Even better, please make it available to sci-hub.
I'd like to better understand why researchers are not taking more of a stand against Elsevier et al.
Not every journal is a viable option, not every journal has the same rating. In theory everyone would prefer to publish in an open access journal but sometimes there's an extra fee for that or there's no good (reputable, good impact factor and appropriate for that topic) open access journal for your project...
While more established researchers can turn down the chance to publish in one of the big, closed journals, it's an opportunity that many cannot turn down.
That means that an amazing paper published in PLoS ONE is generally worse for your career than an unreplicable pile of drek in Nature. The quality of the paper, barring notable impact outliers, is secondary to the journal it's published in.
I agree. And stronger than that, recently some funding agencies (e.g. the Dutch NWO) favor/require open access for works that come out of projects they fund, and they have budget set aside for that. The trend is hopefully changing, but it takes time.
Most people don't.
And many who do avoid it like the plague.
If you would like your work to gather some visibility, I would highly recommend making it available for free, eg publishing the PDF on github.
The ADS1299-4, ADS1299-6, and ADS1299 devices are a family of four-, six-, and eight-channel, low-noise, 24-bit, simultaneous-sampling delta-sigma (ΔΣ) analog-to-digital converters (ADCs) with a built-in programmable gain amplifier (PGA), internal reference, and an onboard oscillator. The ADS1299-x incorporates all commonly-required features for extracranial electroencephalogram (EEG) and electrocardiography (ECG) applications. With its high levels of integration and exceptional performance, the ADS1299-x enables the creation of scalable medical instrumentation systems at significantly reduced size, power, and overall cost.
The product page [1] further states that talks SPI with the host system (an STM32 in this project, nice).
US price for the ADS1299 on Digi-Key seems to be in the region of $50 in single-quantity.
Edit: the paper actually mentions the cost of ironbci to be $350
I read some books by Nicolelis' several years ago and it seemed back then that really useful BCI's still required invasive surgery because not only do you need to gather signals from a large number of neurons you also need to be able to distinguish them (and in EEG the signals are merged together and then further obscured by the effect of the skull etc.)
It will be interesting to see how much adoption Neuralink can get with the invasive approach.
I was shocked at how bad the state of the literature was with so many dubious papers, especially since the explosion of ML techniques.
I came from a Physics background and there you could generally trust the literature was legit and not just smoke and mirrors to get published.
I would not put much (any) stock into this. The army has put out RFPs for using magnetic tourniquets to induce clotting without external pressure. With magic. Just because the military pays someone to try something does not mean that it is not entirely bullshit.
Let’s imagine a different world and maybe we’ll get it
https://horses.extension.org/what-is-the-carbon-footprint-of...
Never liked that quote. It never made any sense to me.
Have you heard of a flash drive lol.
The thing is this would store like your collective knowledge's/notes you've written over the years and it would be secure.
Related to this for the thought of hybrid mind/brain interface is an ideal part of it. Like thinking to control devices by looking at it.
Noninvasive EEG sensors measure the synchronous activity of millions of neurons. While circuit equivalents of individual neurons are a well established idea (see Hodgkin-Huxley models for an early example), this approach does not scale to models of the full brain. There is research into using neural mass or neural field models as generative models of EEG/MEG, but those are statistical models over larger volumes of brain.
For a good overview of current efforts in EEG modeling, you might try this open access paper: https://link.springer.com/article/10.1007/s10548-021-00828-2
The brain is not an electric circuit. It's more like a hydraulic or osmotic circuit. Thought is powered by tiny flows of sodium ions, not flows of electricity. Specifically, sodium diffuses in blood/water much faster than charge dissipates through brain tissue.
When a lot of sodium flows into a cell (a neuron fires) it creates a positive voltage difference across the cell membrane. The concentration of sodium ions floating in the extracellular space remain essentially constant (sodium rapidly flows in from the outside area) even as the charge inside the neuron rises.
So as the neuron is at the peak of its action potential it acts like a 70 mV voltage source relative to the outside of the body. The EEG measures that voltage. It's like holding up a flourescent light near a Tesla coil[1]- electrodes closest to the neuron see a higher voltage from it, because they're measuring the ratio of resistance between 1. The electrode and neuron, through the head, and 2. the resistance to reference ground through the EEG itself. Because the head has quite high resistance (~megaohm?) EEGs require some serious input impedance.
On average neurons spend ~1 out of every ~10,000 milliseconds firing. If you had ~100 electrodes and 1 million neurons, you could triangulate to mostly work out where every single neuron is when it fires. In a human brain, there are ~billion neurons per electrode, with ~100,000 firing at any one time. EEGs are only really picking up on coordinated groups of millions of neurons firing together. Inferring where the actual neurons are is inherently a blurry guess. It is possible, but afaik (and I am very lay) most visualizations only interpolate surface potentials. Implanted electrodes can locate individual neurons, which is incredibly cool.