Recognition of Emotional States Using Fuzzy Convolutional Neural Network Based on Electroencephalography in Different Bands

Emotions are time-varying phenomena that are created as a response to stimuli. In order to continuously detect emotions, the response of brain signals and facial expressions to the video stimulus can be used. In this way, a series of stimulating videos are shown to viewers, and at the same time, their brain signals and facial expressions are continuously recorded, and their capacity level (negative to positive emotions) is recorded.

The purpose of this article is to understand human emotions using the analysis of electroencephalography signals. In this article, it is presented to recognize the emotions using the fuzzy convolutional neural network that selects the optimal and effective features from the electroencephalography signal to recognize and recognize the emotional states of different people. In the proposed method, first the electroencephalography signal will be decomposed into different alpha, beta and gamma bands, and then intelligent diagnosis will be performed.

The experimental results show that the relaxation and boring states are better recognized in the alpha band with 94.2% and 78.8% accuracy, respectively. In the gamma band, happiness is recognized better with 92.2% accuracy, and finally in the beta band, fear will be recognized with 92.3% accuracy. Also, the use of fuzzy logic in the proposed method has increased the recognition accuracy in all bands.

It is concluded that the proposed model using CNN has high accuracy in emotion recognition, in addition, the use of fuzzy classification has significantly increased the accuracy of the model.