Olfactory Perceptual-Ability Assessment by Near-Infrared Spectroscopy using Vertical-Slice based Fuzzy Reasoning

The paper introduced a novel approach for automatic assessment of olfactory perceptual-ability of human-subjects using a functional Near Infrared Spectroscopy device. The assessment requires fuzzy functional mapping from spectroscopic measurement to perceptual-ability using Type-2 fuzzy reasoning. The novelty of the work lies in Vertical Slice Based General Type-2 Fuzzy Reasoning which employs fuzzy meet and union between the planes of type-2 measurement and observation spaces using the classical definition of t-norms and s-norms. The results of the meet and the union computation are later used as the Lower and Upper Firing Strength of the fired rule to determine the structure of the inference. Experiments undertaken confirm the efficacy of the proposed technique over traditional functional mapping, involving neural networks, regression analysis, and the like. The proposed technique of olfactory perceptual-ability can be directly employed to determine the thresholds for recognition-probability and discrimination-probability, when submitted to the subject in presence of aromatic noise. An analysis is undertaken to measure the computational overhead, which is found of the order of O(m.n) and run-time complexity of 94.78 ms, where m and n respectively represent discretizations in the vertical slice and features respectively. A statistical test undertaken confirms the superior performance of the proposed system with others at 95% confidence level

Hemodynamic Analysis for Cognitive Load Assessment and Classification in Motor Learning Tasks Using Type-2 Fuzzy Sets

The paper addresses a novel approach to assess and classify the cognitive load of subjects from their hemodynamic response while engaged in motor learning tasks, such as vehicle-driving. A set of complex motor-activity-learning stimuli for braking, steering-control and acceleration is prepared to experimentally measure and classify the cognitive load of the car-drivers in three distinct classes: High, Medium and Low. New models of General and Interval Type-2 Fuzzy classifiers are proposed to reduce the scope of uncertainty in cognitive load classification due to the fluctuation of the hemodynamic features within and across sessions. The proposed classifiers offer high classification accuracy over 96%, leaving behind the traditional type-1/type-2 fuzzy and other standard classifiers. Experiments undertaken also offer a deep biological insight concerning the shift of brain-activations from the orbito-frontal to the ventro-lateral prefrontal cortex during high-to-low transition in cognitive load. Further, the activation of the dorsolateral prefrontal cortex is also reduced during low cognitive load of subjects. The proposed research outcome may directly be utilized to identify driving learners with low cognitive load for difficult motor learning tasks, such as taking a U-turn in a narrow space and motion control on the top of a bridge to avoid possible collision with the car ahead