DARPA links brain waves, sensors and algorithms to detect targets
September 20, 2012
DARPA has linked human brainwaves, better sensors, and cognitive algorithms to improve target detection with its Cognitive Technology Threat Warning System (CT2WS).
For warfighters operating in the field, the ability to detect threats from standoff distances can be life-saving. When advanced radar and drone coverage is not available, warfighters typically rely on their own vision to scan their surroundings.
Scanning over a wide area, though, is challenging because of the amount of territory that must be reviewed, the limited field of view of the human eye, and the effects of fatigue.
Current technologies like binoculars, cameras, and portable radars can help to improve visibility and increase the threat detection rate. Unfortunately, current miss rates of 47 percent or greater using these technologies leave warfighters unprepared and vulnerable.
DARPA launched the CT2WS program in 2008 with the goal of maximizing warfighters’ awareness of their surroundings by developing man-portable visual threat detection devices.
The CT2WS system includes a 120-megapixel, tripod-mounted, electro-optical video camera with a 120-degree field of view; cognitive visual processing algorithms that can be run on laptops or more powerful computers to identify potential targets and cue images for operator review; and an electroencephalogram (EEG) cap that monitors the operator’s brain signals and records when the operator detects a threat.
Brain waves automatically detect targets
CT2WS built on the concept that humans are inherently adept at detecting the unusual. Even though a person may not be consciously aware of movement or of unexpected appearance, the brain detects it and triggers the P-300 brainwave, a brain signal that is thought to be involved in stimulus evaluation or categorization.
Users wearing an EEG cap are shown about ten images per second, on average. Despite that quick sequence, brain signals indicate to the computer which images were significant.
The use of EEG-based human filtering significantly reduces the amount of false alarms. The cognitive algorithms can also highlight many events that would otherwise be considered irrelevant but are actually indications of threats or targets, such as a bird flying by or a branch’s swaying.
In testing the full CT2WS kit, absent radar, the sensor and cognitive algorithms returned 810 false alarms per hour. When a human wearing the EEG cap was introduced, the number of false alarms dropped to only five per hour, out of a total of 2,304 target events per hour, and a 91 percent successful target recognition rate.
HRL Laboratories, Advanced Brain Monitoring, Quantum Applied Science & Research, and the University of California San Diego developed the system for DARPA.