Gender classification of subjects from cerebral blood flow changes using Deep Learning

Abstract

In this study, using Deep Learning, the gender of subjects is classified the cerebral blood flow changes that are measured by fNIRS. It is reported that cerebral blood flow changes are triggered by brain activities. Thus, if this classification has a high searching accuracy, gender classification should be related to brain activities. In the experiment, fNIRS data are derived from subjects who perform a memory task in white noise environment. From the results, it is confirmed that the learning classifier exhibits high accuracy. This fact suggests that there exists a relation between cerebral blood flow changes and biological information.