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Comment by cryoshon | Hacker News Reader

>It's comparable to the task of reading the contents and activity of a CPU by attaching sensors to the surface - but without knowing the type of CPU, OS, what programs are running, what the user is doing etc.

but you could easily map the bulk EEG data of an individual person in different brain states. just have them go through a calibration routine where they are prompted "think about apples" and then you record what comes back. now, you know what it looks like when they're thinking about apples in an intentional way. it's a start.

there's still plenty of noise, but the signal is going to look the same every time, if you can find it. the other question is whether your detection apparatus is going to be able to tease out the difference between "thinking about apples" versus "thinking about oranges". at present, my guess is no, which speaks to your point.

i get the feeling these problems are going to be solved as time goes on...

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"... where they are prompted "think about apples" and then you record what comes back. "

What if you, one day, bite into an apple that has a worm in? You'll probably feel different about apples after that and you calibration data is now useless.

As I said, just consider the sparse information you get out of an EEG or FMRI and compare it to the vast information that make up your thoughts, memories, feelings etc. It's simple mathematics, it won't work unless you find a way to extract much more data non-invasively. Upon that, you most likely won't get away with a single calibration.

Yes there are awesome results, like controlling a cursor or artificial limbs with your brain. But that only works because your brain learns how to control it - not the other way around.

Continuing that thought, I can imagine a system where the user can learn how to express certain emotions to a computer - but that still leaves open the question of how to induce them into another user, which is a completely different task that we even haven't touched yet at all.

> What if you, one day, bite into an apple that has a worm in? You'll probably feel different about apples after that and you calibration data is now useless.

More likely, what if you think about an apple while hungry. While in a mood for one vs not.

> As I said, just consider the sparse information you get out of an EEG or FMRI and compare it to the vast information that make up your thoughts, memories, feelings etc. It's simple mathematics, it won't work unless you find a way to extract much more data non-invasively.

This reminds me of a study I just found where some researchers, as a thought experiment, tried to reverse engineer a 6502 MOS Tech CPU using the same techniques used in neuroscience. Their argument seems to be that if we can't even reverse engineer a totally known system with our tools, we can hardly claim to be able to reverse-engineer a system whose core 'design' we don't even really have a concept of:

http://journals.plos.org/ploscompbiol/article?id=10.1371/jou...

>The signal is going to look the same everytime.

What makes you think that it will look the same? I don't think it is that deterministic to be practical. Even so, would it generalize to different people?

it'd be calibrated on a per-person basis. if a person can't get it to recognize something that it was correctly identifying previously, then they add the current pattern to the calibration library.

clunky, but it'd work.

No, it would not work. Neither fMRI or "bulk EEG data" (whatever that is) reproduces brain activity patterns for even the most basic ideation, I have no idea why you would think they do. This is fantasy.

Researchers already do this in the neuroimaging community. The technique is called MVPA (see http://www.pymvpa.org/index.html for an implementation). The problems of machine learning in general also apply here, as nom pointed out. Classifiers applied to brain images are only as good as the training datasets used.

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