Brainprints: identifying individuals from magnetoencephalograms

Abstract

Neuroimaging tools have been widely adopted to study the anatomical and functional properties of the brain. Magnetoencephalography (MEG), a neuroimaging method prized for its high temporal resolution, records magnetic field changes due to brain activity and has been used to study the cognitive processes underlying various tasks. As the research community increasingly embraces the principles of open science, a growing amount of MEG data has been published online. However, the prevalence of MEG data sharing may pose unforeseen privacy issues. We argue that an individual may be identified from a segment of their MEG recording even if their data has been anonymized. From our standpoint, individual identifiability is closely related to individual variability of brain activity, which is itself a widely studied scientific topic. In this paper, we propose three interpretable spatial, temporal, and frequency MEG featurizations that we term brainprints (brain fingerprints). We show using multiple datasets that these brainprints can accurately identify individuals, and we reveal consistent components of these brainprints that are important for identification. We also investigate how identification accuracy varies with respect to the abundance of data, the level of preprocessing, and the state of the brain. Our findings pinpoint how individual variability expresses itself through MEG, a topic of scientific interest, while raising ethical concerns about the unregulated sharing of brain data, even if anonymized.