EEG Signal Based Human Emotion Recognition Brain-computer Interface using Deep Learning and High-Performance Computing

The emotion is an indispensable component of human emotion, influencing daily decisions and regular physiological processes. Emotion classification using brain-computer interface (BCI) devices is an exciting, intriguing field of research. Numerous EEG-based human emotion identification systems are examples of recent research work in this field. This paper reviews the development in this area and analyses EEG signals for emotion recognition. This study describes and distinguishes emotion recognition methods with the relevant learning classifier model. The adopted deep learning-based classifier is performing the best compared with the contemporary state of the art models  in terms of all parameters. Every classifier model's effectiveness on the benchmark data set is evaluated using the proposed model. This paper analyses and compares the accuracy of the most well-known learning-based classifier algorithms for emotion recognition, which are the state of art methods. This paper also reviews the research track of emotion classification centered on the EEG signal feature extraction method. This research paper will be utilized to improve automated human rehabilitation engineering and customer-oriented industries.