美国科学家研制解码器：能将脑电波转换成图像
* 美国科学家日前利用电脑模型和一种实时成像扫描系统，成功确定了一个人刚刚看到的图片。电脑重现人大脑中的梦境、想象成为可能。
研究人员证实一种运算法则能将人脑活动转换为图像
网易探索3月13日讯，据《科学美国人》杂志报道，俗话说人心难测，但科技的发展正在令“人心”不再难测。美国科学家日前利用视觉解码器，成功对脑电波信号进行解码，将其转换成了一个人刚刚看到的图片。研究人员表示，这意味着用电脑重现人脑中的梦境、想象或记忆已经成为一种可能。不必说，其潜在用途相当广泛。此成果发表在3月5日出版的《自然》杂志上。
美国加州大学伯克利分校的神经学家杰克·加朗教授在论文中写道：“我们的成果表明，仅仅依靠对人脑活动的监测和分析来重建其感知的视觉图像很快将成为可能。想象一下吧，一个普通的‘读脑’设备随时随地将你看到的图像呈现出来，甚至梦境和想象也有可能变成可视的画面。”
初步测试效果惊人
加朗教授领导的研究小组选择了两名同事接受试验。在第一阶段，他们每人观看了1750张图片，在观看的同时，研究人员利用功能核磁共振成像扫描仪来监测他们观看每一张图片时的大脑视觉皮层的活动情况。
研究人员通过将大脑视觉皮层分成不同的显示栅格立体图层(VOXEL)测量单元，计算出图像各部分所对应的每个视觉皮层片段的反应，建立二者的相关性。基于这样的对应关系，科学家开发了相关的电脑程序，能将脑电波转换成图像。比如，一个显示栅格立体图层(VOXEL)可能反应出特定的图案，而另一显示栅格立体图层(VOXEL)则体现了图片的不同部分。
在第二阶段，两名受试者观看了任意挑选的120张新图片，其中包括动物、建筑、水果、人物和其它物体，同时fMRI扫描仪继续记录他们的大脑信号。通过电脑程序的分析，准确率高达92%。当受试者看到的图片数量增加到1000张时，电脑的表现也有所下降，准确率为80%。当图片数量继续增加时，准确率也会下降。据科学家介绍，当图片数量变为10亿张，电脑对大脑信号“解码”的准确率只有20% 左右。10亿相当于google上能搜索到的图片数量。
前景迷人
虽然新研究离完全揭示人的内心秘密还有很长时间，但是这一技术趋势已引起科学界高度关注。科学家表示此解码技术将有很大的科学和实际应用，可以用它来调查人们不同感受，研究隐藏的精神活动过程，单纯从精神现象来评价视觉内容，如梦境和想像。
不过，有人指出，新技术是一把双刃剑，其好处是:将来可以检测出心藏诡秘的人，审讯犯人变得非常简单。新技术也让神经修复设备成为可能。比如，瘫痪病人尽管脊髓受到损伤，但假肢仍能通过读取大脑的指令，完成一些简单动作。但也有坏处:没有个人隐私的世界会非常可怕，如果做间谍卧底可能无处藏身。
不过，科学家指出，目前的技术只能确认已知的图片，对电脑没有“汇编”的新图片就失灵了。事实上此技术还不能完全识出你最内心的想法，加朗教授开玩笑说，某些心藏诡秘的人要想洗心革面重新做人，还有的是时间。（尼特）
最新回复
superwcm (2008-9-09 20:19:54)
March 6, 2008
Do You See What I See? Translating Images out of Brain Waves
Visual decoder allows researchers to translate brain wave activity into images
By Nikhil Swaminathan
NOT TO WORRY: Researchers demonstrate an algorithm than can indentify images by translating brain activity—still a far cry from reading minds.
iStockphoto/Andrew Howe
File this under futuristic (and perhaps a little scary): In a step toward one day perhaps deciphering visions and dreams, new research unveils an algorithm that can translate the activity in the minds of humans.
Scientists from the University of California, Berkeley, report in Nature today that they have developed a method capable of decoding the patterns in visual areas of the brain to determine what someone has seen. Needless to say, the potential implications for society are sweeping.
"This general visual decoder would have great scientific and practical use," the researchers say. "We could use the decoder to investigate differences in perception across people, to study covert mental processes such as attention, and perhaps even to access the visual content of purely mental phenomena such as dreams and imagery."
The scientists say that previous attempts to extract "mental content from brain activity" only allowed them to decode a finite number of patterns. Researchers would feed image to an individual (or ask them to think about an object) one at a time and then look for a corresponding brain activity pattern. "You would need to know [beforehand], for each thought you want to read out, what kind of pattern of activity goes with it," says John-Dylan Haynes, a professor at the Bernstein Center for Computational Neuroscience Berlin and the Max Planck Institute for Human Cognitive and Brain Sciences that was not affiliated with the new work.
"The advance brought forward here," he continues, "is that they have set up a mathematical model that captures the properties of the visual part of the brain," which can then be applied to previously unseen objects.
Researchers used functional magnetic resonance images (fMRIs) to record activity in the visual cortices of a pair of volunteers (two of the study's co-authors) while they viewed a series of images. They examined the brain by dividing the regions into voxels (volumetric units, or 3-D pixels) and noting the part of the picture to which each section responded. For instance, one voxel, or slice, might respond in a certain pattern to, say, colors in the upper left-hand corner of the photo, whereas another voxel would be set off by something in a different portion of the picture.
Haynes says the team could "go back and infer what the image was that a person was seeing" by monitoring the activity in each brain section and deciphering what sort of information would most likely be found in the corresponding part of the visual field, or photograph.
When the volunteers scanned a new set of 120 images—depicting everything from people to houses to animals to fruit and other objects—the computer program correctly identified what they were looking at up to 92 percent of the time; when the image pool was upped to 1,000, the algorithm was successful 80 percent of the time. Naturally, its accuracy decreased as the number of possible pictures grew, but even at a quantity 100 times greater than the number of images indexed on the Internet by Google, according to the scientists, the model would be successful greater than 10 percent of the time. (This far exceeds the success rate of random guessing.)
"This indicates," the researchers wrote, "that fMRI signals contain a considerable amount of stimulus information and that this information can be successfully decoded in practice."
Haynes says the method is limited to deciphering information that can be mapped out in space, such as sensory inputs (where a sound is coming from) or motor function (what action one's arm has performed). The challenge, he says, is that it cannot "be easily applied to cases where you don't have a clear mathematical model," such as memories, intentions and emotions. "High-level thoughts would be a bit tricky to get a hold of without such a mathematical model," he adds.
So, you can keep that tinfoil helmet in your closet for now. These algorithms still can't read our innermost thoughts—at least not yet.