The Robust EEG Based Emotion Recognition using Deep Neural Network - Barri Khojasteh, Samad; Kahramanli Ornek, Humar (International Journal of Intelligent Systems and Applications in Engineering, 2021)
: In this paper, we proposed a novel EEG based one dimensional convolution neural network to classify emotional states. Differential entropy (DE) has been considered as a feature extraction method after preprocessing phase. In addition, feature smoothing-linear dynamic system (LDS) and then min-max normalization have been used in the DE features before feeding into deep model. We have designed a one dimensional CNN model with six convolutions and fully connected blocks that have been given outstanding performance in six combinations of SEED dataset. Our model presented maximum average accuracy of 98.55% and 95.91% in binary and single sessions respectively by 10 fold cross validation. The proposed results have fully demonstrated that our method achieved excellent performance compare with other EEG based emotion recognition systems. The proposed deep model can be applied to other emotional datasets and also can be used in health care decision making systems.