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?

读心术。你在科幻电影中曾看过：可以读出我们想法的机器。然而，如今有很多机器可以读出我们大脑中的电波。我们称之为「脑波图」。这些脑波中含有信息吗？如果含有信息，我们可以训练计算机读懂我们的思想吗？

My buddy Nathan has been working to hack the EEG to build a mind-reading machine.

我的好友内森一直致力研究如何破解脑波图以建造一台可以读心的机器。

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. 

先介绍一下脑波图的原理。你的头里有大脑，而大脑是由数十亿个神经元构成。每个神经元都在互相传送电子讯息。这些微小的讯息可以结合在一起，形成显示器上探测到的电波。

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?

传统来说，脑波图能告诉我们大维度的事情，例如你是睡着还是清醒着。但是它可以告诉我们其它事情吗？它是否能够读出我们心中所想？我们要测试这一点，而我们不会从一些复杂的想法开始。我们会从一些非常简单的事情开始。我们只需要依据脑波就可以判读一个人看到了什么吗？

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

内森先在克莉丝蒂的头上安装电极。

Nathan: My life is tangled.

内森：我的生命乱成一团。

GG: And then he's going to show her a bunch of pictures from four different categories.

GG：之后他会给她看一些图片，图片来自四个不同类别。

Nathan: Face, house, scenery and weird pictures.

内森：面孔、房子、风景、古怪的图片。

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.

GG：当我们向克莉丝蒂展示数百张这种图片时，内森的计算机捕捉了她的脑波。我们想知道我们能否侦测脑波中有关这些图片的视觉信息。当实验结束后，我们将会看到脑波图是否可以告诉我们克莉丝蒂在看哪种图片，即是不同种类的图片，会否触发不同的大脑信号。

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.

我们收集完了所有原始脑波图数据，这是我们的成果。看上去很混乱，我们来根据图片类别分类。现在，还是有点太杂乱，无法看出任何区别，但是如果将图片出现的时间对齐，并对每种类别的脑波图取平均值，就能移除其中的杂乱。很快我们便从各个类别中看到一些主要的规律。

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.

现在这些信号看起来仍然是很相似。让我们再仔细看看。大约在一张图片出现一百毫秒后，我们在四个类别中都看到了正向波动，我们把它叫作 P100 ，我们认为这是当你识别物体时大脑中发生的活动。但糟糕了，看看面孔图片的信号，它看起来与其他的不同，在图片出现后约 170 毫秒，出现了负向波动。

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.

这里可能发生了什么事？研究显示，我们大脑有大量神经元专门负责识别人类的面孔，所以这个 N170 脑电负成分可能是由这些神经元产生在同一个地方同时启动，而我们可以在脑波图中探测到。

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.

于是这里有两个结论：第一，在没有进行平均法降噪时，我们的眼睛不能识别脑波规律的不同；第二，即使移除噪声后，我们的眼睛也只能识别出和面孔有关的信号。

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?

于是我们在此转而借助机器学习。我们的眼睛并不擅长在噪声中发现规律，但是机器学习算法的设计可以解决这类问题。所以我们能否将许多图片和数据输入到计算机中进行训练，从而实时判断克莉丝蒂究竟正在看什么？

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.

我们尝试将她的脑波图信息进行实时编码，并预测她眼睛在看什么东西。如果这样有效，我们应该能看到每当她看到风景的图片时，机器显示风景、风景、风景、风景，看到面孔──面孔、面孔、面孔、面孔，但是我们发现，实际上并非如此。

OK.

好的。

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

导演：怎么了？GG：我觉得我们应该转行。

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.

好吧，所以刚刚那个是重大失败。但是我们依然好奇：我们能将这项技术推展到多远？于是我们回顾做法。

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.

我们发现数据飞快涌入计算机，但没有对图片出现的时间进行计时，这等同于读一个在单词间没有空格的长句。这样的句子很难读懂，但是只要我们添加了空格，我们就能看到独立的单词，句子也就变得容易理解得多。

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.

但如果我们作一点弊呢？透过使用传感器，我们能告诉计算机每张图片出现的时间。这样，脑波就不再是一个没有间断的信息串流，而是变成了一个个有意义的封包。另外，我们还要再作弊一下，把图片限制到两个类别。让我们看看我们是否能够实时读心。

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."

在这个新实验中，我们将限制实验条件，这样我们就会知道图片出现的时间，并将类别限制为「面孔」或「风景」。

Nathan: Face. Correct. Scenery. Correct.

内森：面孔，正确。风景，正确。

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.

GG：所以现在每当图片出现时，我们对图片出现的时刻进行记录，并对脑波图译码。它变得越来越正确。

Nathan: Yes. Face. Correct.

内森：是的，面孔，正确。

GG: So there is information in the EEG signal, which is cool. We just had to align it to the onset of the image.

GG：所以脑波图的信号中包含信息，这很棒。我们仅仅需要把它和图片出现的时刻对齐。

Nathan: Scenery. Correct. Face. Yeah.

内森：风景，正确。面孔，没错。

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.

GG：这意味着它包含了一些信息，如果我们知道图片出现的时间，我们就有可能判断它是哪个类别的图片，至少一般可以做到，只要根据这些由图片诱发的电位。

Nathan: Exactly.

内森：说得没错。

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.

GG：如果你一开始跟我说，这个计划有可能实现，我会说，怎么可能。我真的觉得我们不可能做到。

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. 

我们的读心术实验真的成功了吗？成功了，但是我们必须作很多弊。结果就是，你能透过脑波图发现一些有趣的事，比如你是否在看某人的脸，但它确实有很多限制。

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.

也许机器学习领域的进步会带来更多重大突破。有朝一日，我们能够解码心中所想。可是就现在来说，当有公司说它能利用你的脑波控制设备，保持怀疑是你的权利和责任。

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