Binghamton University researchers have developed a biometric identification method called Cognitive Event-RElated Biometric REcognition (CEREBRE) for identifying an individual’s unique “brainprint.” They recorded the brain activity of 50 subjects wearing an electroencephalograph (EEG) headset while looking at selected images from a set of 500 images.



The researchers found that participants’ brains reacted uniquely to each image — enough so that a computer system that analyzed the different reactions was able to identify each volunteer’s “brainprint” with 100 percent accuracy.

In their original brainprint study in 2015, published in Neurocomputing (see ‘Brainprints’ could replace passwords), the research team was able to identify one person out of a group of 32 by that person’s responses, with 97 percent accuracy. That study only used words. Switching to images made a huge difference.

High-security sites

It’s only a three-point difference, but going from 97 to 100 percent makes possible a reliable system for high-security situations, such as “ensuring the person going into the Pentagon or the nuclear launch bay is the right person,” said Assistant Professor of Psychology Sarah Laszlo. “You don’t want to be 97 percent accurate for that, you want to be 100 percent accurate.”

Laszlo says brain biometrics are appealing because they can be cancelled (meaning the person can simple do another EEG session) and cannot be imitated or stolen by malicious means, the way a finger or retina can (as in the movie Minority Report).

“If someone’s fingerprint is stolen, that person can’t just grow a new finger to replace the compromised fingerprint — the fingerprint for that person is compromised forever. Fingerprints are ‘non-cancellable.’ Brainprints, on the other hand, are potentially cancellable. So, in the unlikely event that attackers were actually able to steal a brainprint from an authorized user, the authorized user could then ‘reset’ their brainprint,” Laszlo explained.

Analyzing “event-related potential” brain signals

The researchers found in their original study that the key to detecting differences in brain signals was to look at and analyze “event-related potential (ERP) brain signals recorded from each subject. ERPs are brain signals that are triggered by specific events (such as seeing a photo). Unlike EEG signals, ERPs are unique and happen over a period of a few milliseconds.

How ERPs are identified

The researchers used six types of stimuli in the CEREBRE protocol: sine gratings, low frequency words, color versions of black and white images, black and white foods, black and white celebrity faces, and color foods. For the foods and celebrity faces, they used ten tokens of each stimulus type (e.g., 10 different foods). As the authors note in a new paper in The IEEE Transactions on Information Forensics and Security, “We … predict that, while ERPs elicited in response to single categories of stimulation (e.g., foods) will be somewhat identifiable, combinations of ERPs elicited in response to multiple categories of stimulation will be even more identifiable. “This prediction is supported by the likelihood that each category of stimulation will draw upon differing (though overlapping) brain systems. For example, if the sine gratings call primarily upon the primary visual cortex, and the foods call primarily on the ventral midbrain, then considering both responses together for biometric identification provides multiple, independent, pieces of information about the user’s functional brain organization — each of which can contribute unique variability to the overall biometric solution.”

Andrew Hatling/Binghamton University | The New Biometric — Brainprint

Abstract of CEREBRE: A Novel Method for Very High Accuracy Event-Related Potential Biometric Identification

The vast majority of existing work on brain biometrics has been conducted on the ongoing electroencephalogram. Here, we argue that the averaged event-related potential (ERP) may provide the potential for more accurate biometric identification, as its elicitation allows for some control over the cognitive state of the user to be obtained through the design of the challenge protocol. We describe the Cognitive Event-RElated Biometric REcognition (CEREBRE) protocol, an ERP biometric protocol designed to elicit individually unique responses from multiple functional brain systems (e.g., the primary visual, facial recognition, and gustatory/appetitive systems). Results indicate that there are multiple configurations of data collected with the CEREBRE protocol that all allow 100% identification accuracy in a pool of 50 users. We take this result as the evidence that ERP biometrics are a feasible method of user identification and worthy of further research.