Person ıdentıfıcatıon usıng functıonal near- ınfrared spectroscopy sıgnals usıng a fully connected deep neural network

In this study, we investigate the suitability of functional near-infrared spectroscopy signals (fNIRS) for person identification using data visualization and machine learning algorithms. We first applied two linear dimension reduction algorithms: Principle Component Analysis (PCA) and Singular Value Decomposition (SVD) in order to reduce the dimensionality of the fNIRS data. We then inspected the clustering of samples in a 2d space using a nonlinear projection algorithm. We observed with the SVD projection that the data integrity associated with each person is high in the reduced space. In the light of these observations, we implemented a random forest algorithm as a baseline model and a fully connected deep neural network (FCDNN) as the primary model to identify person from their brain signals. We obtained %85.16 accuracy with our FCDNN model using SVD reduction. Our results are in parallel with the neuroscience researches, which state that brain signals of each person are unique and can be used to identify a person.