A New Fractal Pattern Feature Generation Function based Emotion Recognition Method using EEG - Tuncer Turker; Dogan Sengul; Subasi Abdulhamit (Elsevier, 2022)
Electroencephalogram (EEG) signal analysis is one of the mostly studied research areas in biomedical
signal processing, and machine learning. Emotion recognition through machine intelligence plays critical
role in understanding the brain activities as well as in developing decision-making systems. In this
research, an automated EEG based emotion recognition method with a novel fractal pattern feature extraction
approach is presented. The presented fractal pattern is inspired by Firat University Logo and
named fractal Firat pattern (FFP). By using FFP and Tunable Q-factor Wavelet Transform (TQWT) signal
decomposition technique, a multilevel feature generator is presented. In the feature selection phase, an
improved iterative selector is utilized. The shallow classifiers have been considered to denote the success
of the presented TQWT and FFP based feature generation. This model has been tested on emotional EEG
signals with 14 channels using linear discriminant (LDA), k-nearest neighborhood (k-NN), support vector
machine (SVM). The proposed framework achieved 99.82% with SVM classifier.