Brainprint:  Identifying Unique Features of Neural Activity with Machine Learning

Maria Ruiz-BlondetBinghamton University
Negin KhalifianBinghamton University
Blair ArmstrongThe Basque Center on Cognition, Brain and Language
Zhanpeng JinBinghamton University
Kenneth KurtzBinghamton University
Sarah LaszloBinghamton University

Abstract

Can a person be identified uniquely by some feature of their neural activity, as they can be by fingerprints?  If so, 1) what would those features be like and 2) are existing computational methods sufficient to extract them?  Here, we explore these questions by coordinating psychophysiological and machine learning approaches.  We begin with the proposition that one unique feature of individual cognition is the detailed network of concepts, and relationships between concepts, that are present in each individual’s semantic memory.  We then demonstrate that we are able to accurately classify individual unlabeled brain activity—in the form of Event-Related Potentials (ERPs) elicited during a task that probes semantic memory—to the individual it belongs to with several pattern classifiers.  These results demonstrate that it is possible to identify individuals on the basis of unique features of their brain activity.  Biometric applications are discussed.

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Brainprint:  Identifying Unique Features of Neural Activity with Machine Learning (179 KB)



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