A new fractal pattern feature generation function based emotion recognition method using EEG

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 criti-cal 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 ex-traction 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. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This research introduces an automated EEG-based emotion recognition method utilizing a novel fractal pattern feature extraction approach and TQWT signal decomposition technique, achieving a high accuracy of 99.82% with SVM classifier after feature selection and shallow classifiers processing.