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A major problem here isn't just the interface technology, but that we have such a poor understanding of how the brain works (my girlfriend is working on her PhD

by tibbon 16y ago

A major problem here isn't just the interface technology, but that we have such a poor understanding of how the brain works (my girlfriend is working on her PhD in Neuroscience and I hear about this almost daily). Consequently, our interfaces will be crude and clunky at best for a long time. We've barely got the faintest ideas of how to measure some things that would seem really basic. Memory? Perception? Forget it. When using one of these you're basically trying to train your brain to stimulate huge areas to try to get some readout. It happens that we aren't great at this. Even playing a game as basic as Pong is very hard with this type of interface, and it isn't as if we're going to get significantly better at this anytime soon. Its about as graceful as trying to make your entire leg smash forward in order to press a small delicate button. We can hit things, but when you have lots of small delicate buttons, the resolution isn't going to get much better since we're still smashing blindly.

kermit_de_fro 16y ago
To be fair, we can make huge steps forward once the interface technology has improved resolution. I will trust your girlfriend's expertise, but I do know from mine in Machine Learning that we can extract a surprising amount of signal without knowing the neurophysical processes which generate the data. A huge problem right now with the products coming out of Emotive and similar companies is that the level of noise is enormous and the resolution of the sensors is very poor. As we continue to remove the noise from the data (and come up with better sensors!), we can train better and better models to differentiate these various activities. One PI working on this project whose work I am familiar with is Mike D'Zmura at UC Irvine. http://cnslab.ss.uci.edu/muri/research.html http://cnslab.ss.uci.edu/muri/research.html
- tibbon 16y ago
  From a non-scientist's point one issue I can see that humans have problems with relating to these devices is a lack of feedback. I've tried a few of these and it takes a few seconds to know if its working or not. That's a pretty unnatural feeling for a human, since we normally know if an attempt of ours had an action quickly or not. Like you said, there is a huge amount of noise that makes it very difficult, and much of the lag we have with feedback is due to us trying to smooth out this noise and find a pattern. Hopefully one or two smart discoveries can give us a nice little leap ahead forward until we have a better brain model to work with- bringing the technology to at least a semi-usable level since as it stands it is pretty much a novelty at best.

by tibbon 16y ago

kermit_de_fro 16y ago

To be fair, we can make huge steps forward once the interface technology has improved resolution. I will trust your girlfriend's expertise, but I do know from mine in Machine Learning that we can extract a surprising amount of signal without knowing the neurophysical processes which generate the data. A huge problem right now with the products coming out of Emotive and similar companies is that the level of noise is enormous and the resolution of the sensors is very poor. As we continue to remove the noise from the data (and come up with better sensors!), we can train better and better models to differentiate these various activities. One PI working on this project whose work I am familiar with is Mike D'Zmura at UC Irvine. http://cnslab.ss.uci.edu/muri/research.html http://cnslab.ss.uci.edu/muri/research.html

tibbon 16y ago
From a non-scientist's point one issue I can see that humans have problems with relating to these devices is a lack of feedback. I've tried a few of these and it takes a few seconds to know if its working or not. That's a pretty unnatural feeling for a human, since we normally know if an attempt of ours had an action quickly or not. Like you said, there is a huge amount of noise that makes it very difficult, and much of the lag we have with feedback is due to us trying to smooth out this noise and find a pattern. Hopefully one or two smart discoveries can give us a nice little leap ahead forward until we have a better brain model to work with- bringing the technology to at least a semi-usable level since as it stands it is pretty much a novelty at best.