Evaluation of Brain Signal Analysis for Subjective Aesthetic-Appreciation Using Type-2 Fuzzy Sets

The paper presents a notable methodology for automatic classification of aesthetic appraisal levels of various individuals using Electroencephalographic (EEG) signals. A set of visual stimuli containing different facial expressions with varying geometric shapes are utilized for classifying the aesthetic appreciation levels of subjects for three classes: High, Medium, and Low. The brain signals are captured by the EEG system and then fed to the e-LORETA software to determine the highly active brain regions involved to assess the aesthetic appreciation levels. Experiment undertaken infers the activation level of Frontal and Temporal lobes are highly increased for lower aesthetic appreciation otherwise the above mention lobes are less active for high aesthetic appreciation. This work also employs a unique general type-2 fuzzy scheme to categories the intra- and inter-session variation in brain signals due to temporal fluctuation of active brain regions. The proposed classifier produces supercilious classification accuracy with respect to other primitive classifiers. Moreover, a statistical analysis also confirms the superiority of the proposed GT2FS classifier over its competitors. Hence, the proposed method can be effectively utilized to measure subjective aesthetic appreciation quality, of healthy and psychologically disordered people.