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  • Translator: Joseph Geni Reviewer: Krystian Aparta

  • Greg Gage: Mind-reading. You've seen this in sci-fi movies:

  • machines that can read our thoughts.

  • However, there are devices today

  • that can read the electrical activity from our brains.

  • We call this the EEG.

  • Is there information contained in these brainwaves?

  • And if so, could we train a computer to read our thoughts?

  • My buddy Nathan has been working to hack the EEG

  • to build a mind-reading machine.

  • [DIY Neuroscience]

  • So this is how the EEG works.

  • Inside your head is a brain,

  • and that brain is made out of billions of neurons.

  • Each of those neurons sends an electrical message to each other.

  • These small messages can combine to make an electrical wave

  • that we can detect on a monitor.

  • Now traditionally, the EEG can tell us large-scale things,

  • for example if you're asleep or if you're alert.

  • But can it tell us anything else?

  • Can it actually read our thoughts?

  • We're going to test this,

  • and we're not going to start with some complex thoughts.

  • We're going to do something very simple.

  • Can we interpret what someone is seeing using only their brainwaves?

  • Nathan's going to begin by placing electrodes on Christy's head.

  • Nathan: My life is tangled.

  • (Laughter)

  • GG: And then he's going to show her a bunch of pictures

  • from four different categories.

  • Nathan: Face, house, scenery and weird pictures.

  • GG: As we show Christy hundreds of these images,

  • we are also capturing the electrical waves onto Nathan's computer.

  • We want to see if we can detect any visual information about the photos

  • contained in the brainwaves,

  • so when we're done, we're going to see if the EEG

  • can tell us what kind of picture Christy is looking at,

  • and if it does, each category should trigger a different brain signal.

  • OK, so we collected all the raw EEG data,

  • and this is what we got.

  • It all looks pretty messy, so let's arrange them by picture.

  • Now, still a bit too noisy to see any differences,

  • but if we average the EEG across all image types

  • by aligning them to when the image first appeared,

  • we can remove this noise,

  • and pretty soon, we can see some dominant patterns

  • emerge for each category.

  • Now the signals all still look pretty similar.

  • Let's take a closer look.

  • About a hundred milliseconds after the image comes on,

  • we see a positive bump in all four cases,

  • and we call this the P100, and what we think that is

  • is what happens in your brain when you recognize an object.

  • But damn, look at that signal for the face.

  • It looks different than the others.

  • There's a negative dip about 170 milliseconds

  • after the image comes on.

  • What could be going on here?

  • Research shows that our brain has a lot of neurons that are dedicated

  • to recognizing human faces,

  • so this N170 spike could be all those neurons

  • firing at once in the same location,

  • and we can detect that in the EEG.

  • So there are two takeaways here.

  • One, our eyes can't really detect the differences in patterns

  • without averaging out the noise,

  • and two, even after removing the noise,

  • our eyes can only pick up the signals associated with faces.

  • So this is where we turn to machine learning.

  • Now, our eyes are not very good at picking up patterns in noisy data,

  • but machine learning algorithms are designed to do just that,

  • so could we take a lot of pictures and a lot of data

  • and feed it in and train a computer

  • to be able to interpret what Christy is looking at in real time?

  • We're trying to code the information that's coming out of her EEG

  • in real time

  • and predict what it is that her eyes are looking at.

  • And if it works, what we should see

  • is every time that she gets a picture of scenery,

  • it should say scenery, scenery, scenery, scenery.

  • A face -- face, face, face, face,

  • but it's not quite working that way, is what we're discovering.

  • (Laughter)

  • OK.

  • Director: So what's going on here? GG: We need a new career, I think.

  • (Laughter)

  • OK, so that was a massive failure.

  • But we're still curious: How far could we push this technology?

  • And we looked back at what we did.

  • We noticed that the data was coming into our computer very quickly,

  • without any timing of when the images came on,

  • and that's the equivalent of reading a very long sentence

  • without spaces between the words.

  • It would be hard to read,

  • but once we add the spaces, individual words appear

  • and it becomes a lot more understandable.

  • But what if we cheat a little bit?

  • By using a sensor, we can tell the computer when the image first appears.

  • That way, the brainwave stops being a continuous stream of information,

  • and instead becomes individual packets of meaning.

  • Also, we're going to cheat a little bit more,

  • by limiting the categories to two.

  • Let's see if we can do some real-time mind-reading.

  • In this new experiment,

  • we're going to constrict it a little bit more

  • so that we know the onset of the image

  • and we're going to limit the categories to "face" or "scenery."

  • Nathan: Face. Correct.

  • Scenery. Correct.

  • GG: So right now, every time the image comes on,

  • we're taking a picture of the onset of the image

  • and decoding the EEG.

  • It's getting correct.

  • Nathan: Yes. Face. Correct.

  • GG: So there is information in the EEG signal, which is cool.

  • We just had to align it to the onset of the image.

  • Nathan: Scenery. Correct.

  • Face. Yeah.

  • GG: This means there is some information there,

  • so if we know at what time the picture came on,

  • we can tell what type of picture it was,

  • possibly, at least on average, by looking at these evoked potentials.

  • Nathan: Exactly.

  • GG: If you had told me at the beginning of this project this was possible,

  • I would have said no way.

  • I literally did not think we could do this.

  • Did our mind-reading experiment really work?

  • Yes, but we had to do a lot of cheating.

  • It turns out you can find some interesting things in the EEG,

  • for example if you're looking at someone's face,

  • but it does have a lot of limitations.

  • Perhaps advances in machine learning will make huge strides,

  • and one day we will be able to decode what's going on in our thoughts.

  • But for now, the next time a company says that they can harness your brainwaves

  • to be able to control devices,

  • it is your right, it is your duty to be skeptical.

Translator: Joseph Geni Reviewer: Krystian Aparta

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【TED】This computer is learning to read your mind | DIY Neuroscience, a TED series

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    林宜悉   に公開 2018 年 09 月 15 日
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