Towards Gamers’ Experience Level Decoding with Optical Brain Imaging

functional Near-infrared Spectroscopy (fNIRS) is fast becoming an alternative optical neuroimaging modality to functional Magnetic Resonance Imaging (fMRI) owing to its portable, non-constrained environment. With the advent of fNIRS numerous studies, and key populations once considered indecipherable because of multitude operational challenges associated with fMRI such as restriction to stay in a supine position motionless surrounded by huge magnets, can now be read into. In this work, brain activity of participants, who are gamers of varying expertise levels, watch images or videos of a game, League of Legends, is recorded using fNIRS. The participant's fNIRS data is then analysed to distinguish between expertise levels of gamers using a support vector machine classifier with a radial basis function kernel. Our results demonstrate the adequacy of using fNIRS data to distinguish between the expertise levels of participants. In particular, the classification accuracy is optimum for novice vs. intermediate participants followed by novices vs. experts for all experiments. The least accurate classification results obtained are for intermediates vs. experts. An attempt is also made to read from different dimensions of hemoglobin to establish which biomarker best respresents the neural activity in the brain. Since the methods employed are independent of the study, we believe this work has strong implications for a professional's objective assessment which is paramount for those occupations especially associated with greater risks e.g. surgeons, pilots.