Brain-computer interfaces are usually designed to help people, but a new project is using this type of interface inverse, to help computers perform tasks they can’t manage on their own.
Researchers used the interface to sort through satellite images for surface-to-air missiles faster than any machine or human analyst could manage alone.
“With Google, you have to type in words to describe what you’re interested in,” says Paul Sajda, an associate professor at Columbia University. “But let’s say I’m interested in something ‘funny looking.’ ”
Sajda explains that computers struggle to classify images according to this kind of abstract concept, but humans can do it almost instantly. Electrical signals within the brain fire before a person even realizes he’s recognized an image as odd or unusual.
Sajda’s device, called C3Vision (cortically coupled computer vision), uses an electroencephalogram (EEG) cap to monitor brain activity as the person wearing it is shown about 10 images per second. Machine-learning algorithms trained to detect the neurological signals that signify interest in an image are used to analyze this brain activity. By monitoring these signals, the system rapidly ranks the images in terms of how interesting they appear to the viewer. The search is then refined by retrieving other images that are similar to those with the highest rank. “It’s a search tool that allows you to find images that are very similar to those that have grabbed your attention,” says Sajda.
At the speed at which it works, the conscious brain is unable to register a “hit.” But the neurological visual pathways work much faster, says Sajda. The brain produces distinct electrical signals that can be detected and decoded by the 64 EEG electrodes within the cap. “It’s on the edge of the subconscious,” he says.
Sajda and colleagues at Columbia have founded a spinoff company called Neuromatters to commercialize the technology with $4.6 million in funding from the Defense Advanced Research Projects Agency. Along with military applications, Sajda says possible applications might include advanced gaming interfaces and neuro-marketing. “It could be used for getting demographic feedback on how much an advert grabs people’s attention,” he says.
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