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.

脑电图（EEG）信号分析是生物医学信号处理和机器学习中研究最多的领域之一。通过机器智能进行的情感识别在理解大脑活动以及开发决策系统中起着至关重要的作用。在这项研究中，提出了一种基于自动脑电图的情绪识别方法，该方法具有新颖的分形特征提取方法。呈现的分形图案受Firat University Logo启发，并命名为分形Firat模式（FFP）。通过使用FFP和可调Q因子小波变换（TQWT）信号分解技术，提出了一种多级特征发生器。在特征选择阶段，使用了改进的迭代选择器。已经考虑使用浅分类器来表示所提出的基于TQWT和FFP的特征生成的成功。该模型已使用线性判别（LDA），k最近邻（k-NN），支持向量机（SVM）在具有14个通道的情绪EEG信号上进行了测试。提出的框架使用SVM分类器达到了99.82％。