A New Fractal Pattern Feature Generation Function based Emotion Recognition Method using EEG

Abstract

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