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Interesting, a few friends and I attempted to launch a startup a few years back looking to improve meditation and focus using neurofeedback: http://synaptitude.

Interesting, a few friends and I attempted to launch a startup a few years back looking to improve meditation and focus using neurofeedback: http://synaptitude.me/ http://synaptitude.me/ Even went so far as to launch a Kickstarter [1]. We had quite a bit of interest, but we didn't feel we had enough sign ups or people interested to take a risk (all of us having been offered jobs and having student debt). The trouble, is most of these studies have too low of an N number and/or the equipment has trouble at the higher frequencies. That's actually part of why we were launching the startup. We had a way to do error correction for those frequencies and by providing value via applications, we could open a secondary market to do experiments with a larger N number. IMO until something like this exists, I take most studies worth a grain of salt. [1] https://www.kickstarter.com/projects/synaptitude/thinksuite-brainware-not-software?ref=user_menu https://www.kickstarter.com/projects/synaptitude/thinksuite-...

dwaltrip 8y ago
Can you please spell out your technical critique of the equipment? You are saying that they don't reliably measure gamma waves?
- lettergram 8y ago
  To capture gamma waves you need to have something that samples at least 200 times per second on the low end 400 times per second on the higher end. This is a minimum, preferably you'd be sampling 1000 times per second. Most EEGs sample between 240 - 500 times per second. This is due to the way FFTs work. Now this does not include noise OR sensor sensitivity, which for reference you'll see from things like electrical (ranging from 50 - 60 hz), which incidentally is the same as gamma waves. You'll also have movement, heart beats, static, equipment interference, electrical signals from looking at things, etc. Etc. This is why you need more sampling the higher you go, because the sensors have errors due to sensitivity already, plus you add a bunch of interference, at a lower sample rate, than say beta waves (which are 12 - 30 hz). Essentially, the more potential for errors the higher N number you need. I never used gamma waves in experiments due to the lack of statical relevance.
  - petra 8y ago
    What's the technical problem in measuring a differential signal at a 1000 samples/sec, plus removing noise and amplifying it ? It's a common analog electronics problem. And if it's not technically complicated, why does nobody do this ?
    - lettergram 8y ago
      It's a very difficult problem, first lets just look at data throughput: 1000 samples/sec, 4 bytes per sample, 32 nodes - that's 128Kb / second. Not crazy by any means. However, you then have to process that... That's doing an FFT + binning (in the case of brain waves). All doable at probably 60 FPS (where a frame is the prior 1 - 3 seconds). Still not a problem, then try to do error correction! You have 32 sensors, what's real, what's an artifact? That's when things slow way down typically. This type of analysis can be done after the fact and you essentially guess at errors. Removing known issues or more likely from a research perspective they just mark those sections of the data and remove them. Here's where what we built comes in: http://synaptitude.me/blog/using-computer-vision-to-improve-eeg-signals/ http://synaptitude.me/blog/using-computer-vision-to-improve-... The above is all about post processing, however, I managed to improve the method and write a proof-of-concept for a real-time application that removes artifacts in EEG signals, BUT leaves the underlying signal (AKA you don't need to throw away the whole sample). This can function to capture error corrected brain waves at 50 Hz with 512 samples per second + a 50hz video stream. Probably could do more. This is extremely technically complicated and probably took a year or so to develop. This enables real-time stream (50Kb / second) to our hosted server and real-time applications: http://synaptitude.me/blog/view-brainwave-data-thinksuite-io/ http://synaptitude.me/blog/view-brainwave-data-thinksuite-io... Unfortunately, I couldn't find someone willing to support the work further, and even though we had maybe 30 people vouching $300 each. That wasn't enough to continue the work. Most of this work concluded in early 2017. For reference, I may apply for an SBIR grant coming up to fund additional work on this (my wife started graduate school, utilizing EEGs, so it aligns pretty well). Honestly, if anyone is interested in this please let me know. Its an area of interest, just couldn't justify the expense or time investment (now working on this: https://hnprofile.com/ https://hnprofile.com/)
Erlich_Bachman 8y ago
You do know about Muse headband, right? They did exactly this and it works great. The company seems great, solvent, they have just released a new version of the hardware (including an HRV sensor), and it actually works. Not sure why your kickstarter failed, but Muse headband is exactly what you are describing, it's a finished polished product and it actually works (speaking from experience).

dwaltrip 8y ago

Can you please spell out your technical critique of the equipment? You are saying that they don't reliably measure gamma waves?

lettergram 8y ago
To capture gamma waves you need to have something that samples at least 200 times per second on the low end 400 times per second on the higher end. This is a minimum, preferably you'd be sampling 1000 times per second. Most EEGs sample between 240 - 500 times per second. This is due to the way FFTs work. Now this does not include noise OR sensor sensitivity, which for reference you'll see from things like electrical (ranging from 50 - 60 hz), which incidentally is the same as gamma waves. You'll also have movement, heart beats, static, equipment interference, electrical signals from looking at things, etc. Etc. This is why you need more sampling the higher you go, because the sensors have errors due to sensitivity already, plus you add a bunch of interference, at a lower sample rate, than say beta waves (which are 12 - 30 hz). Essentially, the more potential for errors the higher N number you need. I never used gamma waves in experiments due to the lack of statical relevance.
- petra 8y ago
  What's the technical problem in measuring a differential signal at a 1000 samples/sec, plus removing noise and amplifying it ? It's a common analog electronics problem. And if it's not technically complicated, why does nobody do this ?
  - lettergram 8y ago
    It's a very difficult problem, first lets just look at data throughput: 1000 samples/sec, 4 bytes per sample, 32 nodes - that's 128Kb / second. Not crazy by any means. However, you then have to process that... That's doing an FFT + binning (in the case of brain waves). All doable at probably 60 FPS (where a frame is the prior 1 - 3 seconds). Still not a problem, then try to do error correction! You have 32 sensors, what's real, what's an artifact? That's when things slow way down typically. This type of analysis can be done after the fact and you essentially guess at errors. Removing known issues or more likely from a research perspective they just mark those sections of the data and remove them. Here's where what we built comes in: http://synaptitude.me/blog/using-computer-vision-to-improve-eeg-signals/ http://synaptitude.me/blog/using-computer-vision-to-improve-... The above is all about post processing, however, I managed to improve the method and write a proof-of-concept for a real-time application that removes artifacts in EEG signals, BUT leaves the underlying signal (AKA you don't need to throw away the whole sample). This can function to capture error corrected brain waves at 50 Hz with 512 samples per second + a 50hz video stream. Probably could do more. This is extremely technically complicated and probably took a year or so to develop. This enables real-time stream (50Kb / second) to our hosted server and real-time applications: http://synaptitude.me/blog/view-brainwave-data-thinksuite-io/ http://synaptitude.me/blog/view-brainwave-data-thinksuite-io... Unfortunately, I couldn't find someone willing to support the work further, and even though we had maybe 30 people vouching $300 each. That wasn't enough to continue the work. Most of this work concluded in early 2017. For reference, I may apply for an SBIR grant coming up to fund additional work on this (my wife started graduate school, utilizing EEGs, so it aligns pretty well). Honestly, if anyone is interested in this please let me know. Its an area of interest, just couldn't justify the expense or time investment (now working on this: https://hnprofile.com/ https://hnprofile.com/)

Erlich_Bachman 8y ago

You do know about Muse headband, right? They did exactly this and it works great. The company seems great, solvent, they have just released a new version of the hardware (including an HRV sensor), and it actually works. Not sure why your kickstarter failed, but Muse headband is exactly what you are describing, it's a finished polished product and it actually works (speaking from experience).