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.