FNIRS User Identification
This paper discusses the potential of functional near-infrared spectroscopy (fNIRS) brain-computer interfaces (BCIs) to identify an individual using only her brain data. fNIRS is a lightweight, portable, non-invasive functional neuroimaging tool that uses light to capture hemodynamic responses in the brain. We show that among 30 subjects, it is possible to determine the subject from whom a segment of the fNIRS data originated with 63% accuracy. Random chance is 3.3% for 30 subjects. Additionally, we explore the effect of the fNIRS brain data window size used during feature construction, on the classification accuracy.
User Identification from fNIRS Data Using Deep Learning
Denisa Qori McDonald, and Erin T. Solovey. User Identification from fNIRS Brain Data Using Deep Learning. Proc. of Neuroadaptive Technology Conference. Berlin, Germany, 2017