P-300 and N-400 Induced Decoding of Learning-Skill of Driving Learners Using Type-2 Fuzzy Sets
Lidia Ghosh, Amit Konar, Pratyusha Rakshit, Sricheta Parui, Anca Ralescu, Atulya K. Nagar · 2018
The paper aims at classifying the learning-skill of driving-learners by utilizing the acquired P300 and N400 event related potentials during the learning phase of a simulated car-driving task. A set of driving stimuli, including braking on sudden appearance of bumpers, steering control at the sharp bending points, and the like, are prepared to experimentally classify the learning skill of the subjects in three distinct classes: Low, Medium and High. The complexity in classifying the subjective learning-skill arises because of intra- and inter-session variations in brain signals due to temporal fluctuation in activation levels of the involved brain lobes. A novel z-slice based model of general type-2 fuzzy set is proposed to represent the intra- and inter-session variations by the footprint of uncertainty and their composite variations by z-slices at different values of a given feature. Further, a novel technique for z-slice based classifier is proposed to classify the learning-skill under intra-and inter-session uncertainty. The proposed classifier is found to outperform traditional and well-known type-2 classifiers for the present learning-skill classification task in Experiments undertaken confirm the frontal and parietal theta (4-7 Hz) activation in discrete learning steps. The proposed research outcome may directly be utilized to certify driving learners with required learning skills.