In Brainprint, a newly published study in Neurocomputing, researchers from Binghamton University, New York observed the brain signals of 45 volunteers as they read a list of 75 acronyms, such as FBI and DVD.
They are said to have recorded the brain’s reaction to each group of letters, focusing on the part of the brain associated with reading and recognising words, and found that participants’ brains reacted differently to each acronym, enough that a computer system was able to identify each volunteer with 94 percent accuracy. The results suggest that brainwaves could be used by security systems to verify a person’s identity.
According to Sarah Laszlo, assistant professor of psychology and linguistics at Binghamton University and co-author of Brainprint, brain biometrics are appealing because they are cancellable and cannot be stolen in the way fingerprint or retina recognition can.
“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 authorised user, the authorised user could then ‘reset’ their brainprint,” Laszlo said in a statement.
Zhanpeng Jin, assistant professor at Binghamton University’s departments of Electrical and Computer Engineering, and Biomedical Engineering, doesn’t see brainprint as the kind of system that would be mass-produced for low security applications - at least in the near future - but it could have important security applications.
“We tend to see the applications of this system as being more along the lines of high-security physical locations, like the Pentagon or Air Force Labs, where there aren’t that many users that are authorised to enter, and those users don’t need to constantly be authorising the way that a consumer might need to authorise into their phone or computer,” Jin said.
The project is funded by the US National Science Foundation and Binghamton University’s Interdisciplinary Collaboration Grants (ICG) Program.
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