Their website has a better description of how it works that is less ambiguous: http://gallantlab.org/
The description of the first video:
"The left clip is a segment of the movie that the subject viewed while in the magnet. The right clip shows the reconstruction of this movie from brain activity measured using fMRI. The reconstruction was obtained using only each subject's brain activity and a library of 18 million seconds of random YouTube video that did not include the movies used as stimuli. Brain activity was sampled every one second, and each one-second section of the viewed movie was reconstructed separately."
So they gathered a lot of fMRI data from people watching several hours of YouTube videos (the training set). They then use this to train some sort of machine learning algorithm to make a model. The pictures you see in the article are from a running the model on a test set which does not contain any of the videos from the training set.
So in essence, the researchers are intercepting network traffic from the visual cortex while a subject is given a certain stimulus, then matching that traffic signature with signatures of similar stimuli. Which is to say, they're doing some very interesting traffic analysis, but aren't actually decoding any of the information itself.
On the other hand, if you have the traffic analysis done well enough, the information decoding is unimportant. Given the brain's pretty strong region specific activity, what parts of the brain are acting up are a pretty good correlate of the data in the stimulus.
To borrow and extend an example from Ender's Game, if you know what the train schedules are, you can figure out troop movements; even if you don't necessarily know which particular unit is going to which place, you still have a pretty good idea of what the military is gearing up for.
They are decoding it using their pre-computed lookup table. This is a very valid approach, since the fMRI signal is both slow and low resolution. It would be awesome to be able to record individual neuron firing en masse and in vivo, but we are not there yet.
"The reconstructed videos are blurry because they layer all the YouTube clips that matched the subject's brain activity pattern." – What does this mean exactly? This is beginning to sound more like a cool machine learning trick and less like mind reading.
This line means this study is much less interesting than it sounds at first. Basically, it sounds like they used the fMRI voxel data to build a classifier that predicted which frames from the videos were showing, and composited each frame weighted by its probability. In other words, wtf.
This link will decay in the future, but http://gallantlab.org/ is the original source. For a similar image of a bird, it has the text: "The left clip is a segment of the movie that the subject viewed while in the magnet. The right clip shows the reconstruction of this movie from brain activity measured using fMRI. The reconstruction was obtained using only each subject's brain activity and a library of 18 million seconds of random YouTube video that did not include the movies used as stimuli. Brain activity was sampled every one second, and each one-second section of the viewed movie was reconstructed separately." There's also a useful video.
It seems valid to me. There's no reason to ask them to somehow extract this visual data "unbiased", without bootstrapping off of video clips like that.
Actually, I'd commend that link to anybody posting complaints here, it covers everything everybody is saying as of my writing here.
> It seems valid to me. There's no reason to ask them to somehow extract this visual data "unbiased", without bootstrapping off of video clips like that.
I agree that their approach seems valid. There is a reason to ask them to extract the visual data in an even more unbiased fashion, though: if we understand how the brain is wired, then it should be "trivial" to back out the image from the patterns of activation.
Of course, the previous sentence is making a couple assumptions that I don't think are anywhere close to valid. 1) "the brain" implies that there is a single, nearly completely conserved architecture that is remotely similar from one person to another; 2) I think you'd need to get the activity to much higher resolution than fMRI can give you; 3) the stimulus <--> response mapping is moderately close to bijective, so for a given input, there's only one set of activity, and vice versa. Still, this study is an interesting first step on what will, no doubt, be a very long journey to improve the technology.
While I think you're right, this is still pretty astonishing.
It's important to note that they're generalizing from a few hours of training data to millions of videos. So the classifier has to be picking up on something deep for it to be re-applied in such a flexible way.
I sort of imagine this approach as being akin to the way Bumblebee (the yellow VW Beetle in the first Transformers) lost his voice, but was able to communicate by switching between radio stations. As that recomposition process being richer and richer, it starts to approximate the real signal...
It could be a step towards mind-reading. Brainwaves are very mysterious; being able to correlate images to brainwaves, even if in a "hackish" manner, could be an important first step to decoding brainwaves.
It could be a step towards seeing what someone is seeing. It's not really mind reading. It's not reading "thoughts", it's reading neural response to visual stimuli. It's cool, but it would just be easier to turn around and look at what the person is looking at, than to hijack the response.
It's only an uneducated guess, but if the visual part of thoughts are generated in the visual cortex, then the device might be able to see those thoughts.
On the other hand, this whole thing is starting to look like the Deus Ex Trailer...
For me, the question is how similar is this stimulation to what happens during dreaming. Netflix would have no draw for me when compared to my own dreams (let alone somebody else's)!
It seems to me that labs at Berkeley always gets a pass when it comes to vastly overstating the significance of their work. This is so far from actually reading the visual data from the brain that even mentioning the future of reading memories seems ludicrous.
The article is misleading. There is not deconstruction or decoding of brain waves happening, it's simply correlating stimuli with prior learned/trained images. If the model was trained on images of dogs and cats, the "reconstructed" images will be terms of dogs and cats, which is the basic limitation of the process.
Source: "In practice fitting the encoding model to each voxel is a straightforward regression problem. " (http://gallantlab.org/)
The reconstructed image would also be in terms of the abstract features of dogs and cats: the shades of color, contours of their bodies, their position on the screen. And the abstract features could be recombined into an average image that's completely unlike any dog or cat but resembles what a person is looking at.
Among the caveats: really high-T magnet, not feasible for general use; the visual stimulus was actually a warped version that would be reproduced as the nicely shaped activation upon retinotopic projection (it's a log-polar mapping)... Still really cool.
(ps: no-paywall version here: surfer.nmr.mgh.harvard.edu)
The first thing that occurred to me, is "How do you show video to people in an MRI?" My scan involved wearing headphones made entirely of plastic[1] due to the fact you're inside a ridiculously strong magnetic field. They didn't actually contain the audio speakers - they were acoustically coupled to something a few metres away.
I suppose if it were properly anchored / built into the machine, you might be able to mount an LCD panel, and then somehow calibrate around it. Alternatively, something complicated involving projection and mirrors, maybe. The scanning tunnel is pretty damn narrow though.
It's strange, but when reading about all sorts of interesting science, I end up wondering about the methodology sometimes more than the actual results.
Having done this (showing video to people in an MRI), I think I'm qualified to respond.
There are two methods we've used, one was a goggle system where an array of optical fibers, one per pixel, are brought from the scanner tube to the control room and coupled to a LED display. The resolution is atrocious, and the thing is heavy, but it gets attached to the head coil so the person inside does not have to bear the weight.
The method we are using now is to attach a mirror to the head coil, and have a huge flatscreen outside the tube. You show mirrored images on the screen, and since the screen is big enough it covers the entire visual field. Works better.
Might I suggest a projector, with a zoom showing on a small piece of frosted plexi? I have used this method to project an image onto a "screen" that was underwater (and quite invisible until struck by the projector beam).
OK, I think this is a little misleading at a glance. From my read they didn't show a video to the subject then create images from the brainwaves. Instead they showed videos to subjects, recorded the response. This allowed them to greate a mapping of response to video. You then show videos later, read the response then look up the video.
The article states; "The reconstructed videos are blurry because they layer all the YouTube clips that matched the subject's brain activity pattern."
They could have just shown the best match, this would not have been a cool blurry image that is very easy to misinterpret as being generated from the brainwaves directly. More than a little slippery.
"The reconstruction was obtained using only each subject's brain activity and a library of 18 million seconds of random YouTube video that did not include the movies used as stimuli."
Not totally scifi awesome, but still pretty cool and I think it is a valid approach. Sounds like the bigger the library of clips mapped to brain activity, the more the technique converges to a desirable result.
An average image leaves you with the abstract features common to all the matches, which tells you far more about what the brain-traffic is up to than showing a single match. And being able to look at that is astonishing in its own right.
It's easy for laypeople to misinterpret things they don't fully understand, but I think they can be disabused of misunderstanding by careful explanation. And we can get from here to there without invoking slipperiness.
As they mention in the article, it is known that dream imagery does invoke responses in the visual cortices (i believe even in V1), however the responses are weak in the early cortices, so it's not currently possible to "read" the dream imagery (i assume they would have done it if possible).
I am not sure how well the algorithm would work without the V1 activation, since V1 is retinotopically organized, making it quite easy to decode.
So V1 is 'raw' and later cortices have performed more processing on the data causing it to be higher level, which in turn makes it harder to translate it back to a visual?
Yes that's more or less the picture. The extraction of images from V1 has been performed before, the novelty in this paper is that they reconstruct motion too. In the case of dreams, the flow of information is in the reverse, higher level areas projecting to lower level, creating the illusion of vision. It's not yet known if an actual image is formed in low level virtual cortices from dreams.
It doesn't surprise me that they're able to do this. What does surprise me is how much the composite video looks like something out of Minority Report. It fulfills my fantastical right-brained expectation of what this kind of "mind reading" should look like—blurry, disjointed, imprecise impressions of a scene—and also makes sense to my left brain as I try to imagine the underlying machine learning algorithms.
The straight lines in the background in the reconstructed video seem unrealistic. If my understanding of human vision is correct, we don't see things like a 2D pixelated display but with varying degrees of focus on items, backgrounds, and people. It's not like I see a straight line on a wall or floor and my brain does Bresenham's.
It's kind of a similar hack. There's a many to one relationship of images to a tag. Then that relationship is reversed and averaged out to get a consensus image. Of course this only works at the linguistic/labelling level, not at a brain level.
I have been wondering for some time if it would be possible to find a correlation between some brain imaging techniques (MRI, PET...) and the act of consciously lying. This could create the ultimate lie-detecting machine.
Potentially, it could be much easier than recognizing full images, as there would only be two possible outputs to discriminate.
You mean that we already tried to locate them and failed, or that we should start looking?
Anyway, my point is that if there are some neural activity pattern that is correlated to lying, it could be easier to extract that information than it is extracting full images from the visual cortex.
This is cool engineering work, sure to generate inflated headlines. Nevertheless, one would use a similar approach to read dreams, if only the detectors could detect the brain activity in V1 during sleep. Idea for creepy pillows??
The description of the first video: "The left clip is a segment of the movie that the subject viewed while in the magnet. The right clip shows the reconstruction of this movie from brain activity measured using fMRI. The reconstruction was obtained using only each subject's brain activity and a library of 18 million seconds of random YouTube video that did not include the movies used as stimuli. Brain activity was sampled every one second, and each one-second section of the viewed movie was reconstructed separately."
So they gathered a lot of fMRI data from people watching several hours of YouTube videos (the training set). They then use this to train some sort of machine learning algorithm to make a model. The pictures you see in the article are from a running the model on a test set which does not contain any of the videos from the training set.