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NeuraLink is using a specialized robot to surgically insert very thin electrodes into the brain. This device here uses good old wet or dry EEG electrodes that

NeuraLink is using a specialized robot to surgically insert very thin electrodes into the brain. 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).

roughly 3y ago
As a bad analogy, I've heard the difference between EEG and implants described as pointing microphones at the outside of a building vs having them inside the room.
- hasmanean 3y ago
  Well at least you can tell if there’s a party going on in room 106.
- plastic-enjoyer 3y ago
  Well, EEG is more like undirected microphones that record the voices of several thousand people at the same time outside of a building, whereas implants are more like directed microphones inside the building that only record the voices of a few people...
  - nextaccountic 3y ago
    It's worth noting that if each electrode is thin enough to be placed in extracellular medium getting signals from several neurons around it, you can do some fairly simple signal processing to separate the signals of individual neurons. This is called spike sorting [0] (a "spike" is an impulse transmitted by a single neuron). 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 http://www.scholarpedia.org/article/Spike_sorting [1] https://github.com/akcarsten/spike_sorting https://github.com/akcarsten/spike_sorting [2] https://towardsdatascience.com/using-signal-processing-to-extract-neural-events-in-python-964437dc7c0?gi=bf8f08a89753 https://towardsdatascience.com/using-signal-processing-to-ex... [3] https://towardsdatascience.com/whos-talking-using-k-means-clustering-to-sort-neural-events-in-python-e7a8a76f316 https://towardsdatascience.com/whos-talking-using-k-means-cl... [4] https://en.wikipedia.org/wiki/Local_field_potential https://en.wikipedia.org/wiki/Local_field_potential [5] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657693/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657693/
    - ron_87 3y ago
      Sounds great! thank you, you seem to be good at this
      - nextaccountic 3y ago
        Not really, I just took a couple of undergraduate courses in neuroscience, but I didn't major in it or anything. For a time I thought about doing research in this area, but I got sick of how the lab animals were treated. Take a look at this https://en.wikipedia.org/wiki/Remote_control_animal https://en.wikipedia.org/wiki/Remote_control_animal
brcmthrowaway 3y ago
How does the neuralink get an advantage though, do neuron spike signals contain any information

roughly 3y ago

As a bad analogy, I've heard the difference between EEG and implants described as pointing microphones at the outside of a building vs having them inside the room.

hasmanean 3y ago
Well at least you can tell if there’s a party going on in room 106.
plastic-enjoyer 3y ago
Well, EEG is more like undirected microphones that record the voices of several thousand people at the same time outside of a building, whereas implants are more like directed microphones inside the building that only record the voices of a few people...
- nextaccountic 3y ago
  It's worth noting that if each electrode is thin enough to be placed in extracellular medium getting signals from several neurons around it, you can do some fairly simple signal processing to separate the signals of individual neurons. This is called spike sorting [0] (a "spike" is an impulse transmitted by a single neuron). 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 http://www.scholarpedia.org/article/Spike_sorting [1] https://github.com/akcarsten/spike_sorting https://github.com/akcarsten/spike_sorting [2] https://towardsdatascience.com/using-signal-processing-to-extract-neural-events-in-python-964437dc7c0?gi=bf8f08a89753 https://towardsdatascience.com/using-signal-processing-to-ex... [3] https://towardsdatascience.com/whos-talking-using-k-means-clustering-to-sort-neural-events-in-python-e7a8a76f316 https://towardsdatascience.com/whos-talking-using-k-means-cl... [4] https://en.wikipedia.org/wiki/Local_field_potential https://en.wikipedia.org/wiki/Local_field_potential [5] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657693/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657693/
  - ron_87 3y ago
    Sounds great! thank you, you seem to be good at this
    - nextaccountic 3y ago
      Not really, I just took a couple of undergraduate courses in neuroscience, but I didn't major in it or anything. For a time I thought about doing research in this area, but I got sick of how the lab animals were treated. Take a look at this https://en.wikipedia.org/wiki/Remote_control_animal https://en.wikipedia.org/wiki/Remote_control_animal

brcmthrowaway 3y ago

How does the neuralink get an advantage though, do neuron spike signals contain any information