Conference proceeding
Towards Garners' Experience Level Decoding with Optical Brain Imaging
2019 11TH COMPUTER SCIENCE AND ELECTRONIC ENGINEERING (CEEC), pp.47-52
Computer Science and Electronic Engineering Conference
01 Jan 2019
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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 INIRS 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 garners 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 gainers 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.
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Details
- Title
- Towards Garners' Experience Level Decoding with Optical Brain Imaging
- Creators
- Mehrin Kiani - Univ Essex, Colchester, Essex, EnglandJavier Andreu-Perez - Univ Essex, Colchester, Essex, EnglandHani Hagras - Univ Essex, Colchester, Essex, EnglandAna R. Andreu - Univ Granada, Granada, SpainMaria Pinto - Univ Granada, Granada, SpainJaime Andreu - Univ Granada, Granada, SpainPratusha Reddy - Drexel Univ, Philadelphia, PA 19104 USAKurtulus Izzetoglu - Drexel UniversityIEEE
- Publication Details
- 2019 11TH COMPUTER SCIENCE AND ELECTRONIC ENGINEERING (CEEC), pp.47-52
- Series
- Computer Science and Electronic Engineering Conference
- Publisher
- IEEE
- Number of pages
- 6
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Identifiers
- 991019170350904721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic