EEG channels with low-cost PiEEG device
pieeg.com
pieeg.com
The Olimex ones are 10-bit (supporting both active and passive electrodes), while it's claimed[0] that 12-bit resolution is sufficient for EEG.
However, it's worth noting that there are obvious advantages to the higher resolution.
As his link makes clear, your ADC just needs enough resolution to measure the signal when the (amplified) offset voltages don't saturate it.
I'm not a EEG designer but I've experimenting and learning about it. Sounds like you make PiEEG? This is really cool and I appreciate the docs on the github.
After amplification, you could get the signal 0-100uV to ~0-Vanalog; so then the entire 8-bit range could ideally be used. Realistically, after filtering, due to roll-off / artifacts one could probably use 3/4ths of that space -- so ~ 192 voltage levels that can be determined by the ADC per sample. Does this sound right?
It'd probably depend on what level of control / reliability one would want from the EEG data; I imagine that you could probably cut a lot of corners if, say, you were only measuring for hemispheric coherence in a small set of frequency bands, or stages of REM sleep (and maybe ERPs like p300).
I don't know, this is in my upcoming experiments. Originally this was an attempt to build an EEG amplifier / filter circuit for an atmega328p 10-bit ADC but for my purposes I settled for a 12-bit (and possibly hardware oversampling) on an EFM32.
But yeah you definitely get what you pay for with EEG. In the lab I'm in, we have a $150k EGI system and a $40k cognionics system and even the cognionics system is way more glitchy/low quality compared to the more expensive systems.
Also, I wonder what happens safety/electrical noise-wise if you're plugging a raspberry pi into the wall? Generally, EEG systems are either battery-powered or use an isolation transformer. I wonder if it's best to power the pi with a battery.
Consider how long it took to start suspecting football and other low impact head trauma of causing CTE, and to start believing in CTE at all. Personally, with a phd in neurobiology and no real data besides gut feeling, I would not be comfortable with any system that wasn't thoroughly shown to be mostly harmless over several decades at least
That said, doing an EEG on yourself is a bit disappointing imo; You won't get much interesting output.
8 channel headband with individual gel packets for each electrode. You pop the gel packets and then you have wet electrodes!
The company is called Ceribell; Happy to answer more questions if you have them. We do not sell to individuals, but if you're in the research space, I'm happy to get in touch.
This device here uses good old wet or dry EEG electrodes that are touching the outside of the head (e.g. on an EEG cap).
So, continuing with the microphone analogy, with the right setup, recording a few voices is almost as good as recording each voice separately. It's not perfect because sometimes people talk over each other, and people distant from the mic may have muffed voices, and stuff like that. But generally speaking, we can roughly understand each individual people in a recording, because each people speak in a different way (and just like that, neurons spike in a different way).
Here's some academic code that does this [1] (it has accompanying articles [2] [3]) (Random article found in Google, no affiliation).
The first thing it does is removing the low frequency component of the signal (the LFP [4], that doesn't originate from any specific neuron), then it stores each individual spike waveform as a time series (a vector of N dimensions, where each dimension is the value of the signal at a moment in time), and then just does some k-means clustering to separate spikes in clusters, then assign each cluster to an individual neuron (by hypothesizing that each neuron likes to spike in a specific way which depends on its characteristics). This is all very vanilla / undergraduate stuff, and it's been done for more than 10 years [5] or perhaps even 20 years or more.
Trouble is, with EEG all you have is essentially an average of the LFP over a large area; you don't get single-neuron spikes. So EEG fundamentally can't listen to each neuron individually. However it isn't right to simply throw away LFP information, and you can still do stuff with it.
[0] http://www.scholarpedia.org/article/Spike_sorting
[1] https://github.com/akcarsten/spike_sorting
[2] https://towardsdatascience.com/using-signal-processing-to-ex...
[3] https://towardsdatascience.com/whos-talking-using-k-means-cl...