7 ms·

Most if not all of the very interesting signals are in the 100hz - 10Khz range. These signals are also very low voltage so. Add in that the skulls acts as a low

Most if not all of the very interesting signals are in the 100hz - 10Khz range. These signals are also very low voltage so. Add in that the skulls acts as a low pass filter and you end up in a situation where you just will not be able to recover the information needed to decode what the brain is doing at the required resolution. You can use ECoG which is like EEG but placed on under dura-mater (a protective bag around the brain) but this is also highly invasive. Also the spatial resolution is pretty bad. You can get a hundred or so sensors in a few square centimeters.

PH01 6y ago
What signals do you mean? Brain waves measured in EEG typically range from 0.5 Hz and above.
- bognition 6y ago
  The signals you pick up between 0.5 Hz and 40Hz are mostly likely "population" level signals or put another way the average activity of millions of neurons spread across large swaths of the brain. A good analogy re: trying to decode the brain from EEG would be akin to trying to decode what is going on in a soccer match but only using the noise of the crowds.
  - PH01 6y ago
    How are signals up to 10Khz generated? I thought the absolute refractory period of a neuronal action potential limits the rate at which they can fire.
    - mattkrause 6y ago
      From start to finish, an individual action potential lasts about 1ms, followed by a few millisecond refractory period. There is probably not much biologically-relevant activity happening at faster timescales. However, one often wants to "sort" spikes so that those arising from individual neurons are grouped together. To do so, you need to sample fast enough to capture the shape of the action potential, not just the fact that it happened. This usually requires sampling rates in the 10s of kHz. (~30 kHz would be good).
      - PH01 6y ago
        So, to be clear, the signal of interest does not go above 1kHz. Obviously if you want to sample a signal you have to use a higher frqeuency.
        mattkrause 6y ago
        Eh, it depends on what you're calling "the signal of interest." A spike train, the output of a neuron, can reasonably be represented as a binary signal (1=spike, 0=no spike) at 1 kHz. In fact, I'd say this is nearly standard. The neuron's membrane potential, a combination of its current inputs, intrinsic properties, and recent history, changes on faster timescales. Whatever processing the brain does probably does not use this information itself, but one probably wants access to it anyway for experimental and practical reasons. The signal that an extracellular electrode picks up isn't that 1 kHz spike train. It's many of them mixed together and you'll need finer features to tell them apart. In other words, you can't just invoke Nyquist, sample at 2 kHz, and call it a day.
        PH01 6y ago
        Quite a lot simpler to seperate the mixed signals using a modern multi-electrode array.
        Balgair 6y ago
        I've written multi-tetrode cluster analysis SW. It is very much not simple. Deconvolving all the inputs is terribly difficult and computationally expensive. Generally we had undergrads as classifiers for the clustering ML, which never really worked all that well, TBH. The higher the sampling frequency, the better the spatiotemporal resolution, but the more data to work through. When you do get a cluster, the overlaps are very close together and it really is hard to say that they are 2 neurons. It takes a lot of data/time to be certain. One thing to remember is that noise is a huge problem with all this. Not just the 'normal' electronic sources (how do you ground a brain well? It's intentionally evolved to be nothing but loops!), but the neuronal too. Neurons will often fire just because (or not), they will jiggle with animal movement, they will move with heartbeat, and they will die off or move on their own, the electrode dances about, the brain attacks the electrodes, some portion of the electrode snaps off, etc. It is a very hard thing to manage well. Sorry, bad 'memories' of those projects.

PH01 6y ago

What signals do you mean? Brain waves measured in EEG typically range from 0.5 Hz and above.

bognition 6y ago
The signals you pick up between 0.5 Hz and 40Hz are mostly likely "population" level signals or put another way the average activity of millions of neurons spread across large swaths of the brain. A good analogy re: trying to decode the brain from EEG would be akin to trying to decode what is going on in a soccer match but only using the noise of the crowds.
- PH01 6y ago
  How are signals up to 10Khz generated? I thought the absolute refractory period of a neuronal action potential limits the rate at which they can fire.
  - mattkrause 6y ago
    From start to finish, an individual action potential lasts about 1ms, followed by a few millisecond refractory period. There is probably not much biologically-relevant activity happening at faster timescales. However, one often wants to "sort" spikes so that those arising from individual neurons are grouped together. To do so, you need to sample fast enough to capture the shape of the action potential, not just the fact that it happened. This usually requires sampling rates in the 10s of kHz. (~30 kHz would be good).
    - PH01 6y ago
      So, to be clear, the signal of interest does not go above 1kHz. Obviously if you want to sample a signal you have to use a higher frqeuency.
      - mattkrause 6y ago
        Eh, it depends on what you're calling "the signal of interest." A spike train, the output of a neuron, can reasonably be represented as a binary signal (1=spike, 0=no spike) at 1 kHz. In fact, I'd say this is nearly standard. The neuron's membrane potential, a combination of its current inputs, intrinsic properties, and recent history, changes on faster timescales. Whatever processing the brain does probably does not use this information itself, but one probably wants access to it anyway for experimental and practical reasons. The signal that an extracellular electrode picks up isn't that 1 kHz spike train. It's many of them mixed together and you'll need finer features to tell them apart. In other words, you can't just invoke Nyquist, sample at 2 kHz, and call it a day.
        PH01 6y ago
        Quite a lot simpler to seperate the mixed signals using a modern multi-electrode array.
        Balgair 6y ago
        I've written multi-tetrode cluster analysis SW. It is very much not simple. Deconvolving all the inputs is terribly difficult and computationally expensive. Generally we had undergrads as classifiers for the clustering ML, which never really worked all that well, TBH. The higher the sampling frequency, the better the spatiotemporal resolution, but the more data to work through. When you do get a cluster, the overlaps are very close together and it really is hard to say that they are 2 neurons. It takes a lot of data/time to be certain. One thing to remember is that noise is a huge problem with all this. Not just the 'normal' electronic sources (how do you ground a brain well? It's intentionally evolved to be nothing but loops!), but the neuronal too. Neurons will often fire just because (or not), they will jiggle with animal movement, they will move with heartbeat, and they will die off or move on their own, the electrode dances about, the brain attacks the electrodes, some portion of the electrode snaps off, etc. It is a very hard thing to manage well. Sorry, bad 'memories' of those projects.