Research Status and Development Trend of EEG Technology for Deep Learning in Emotion Recognition

Abstract

EEG technology has made significant progress as people's attention to their emotions increases. This has also led to significant progress in various brain sciences, as well as their correlation with emotions. Nevertheless, based on the development of contemporary brain science, it is not possible to provide detailed cause analysis or draw accurate conclusions based on EEG information for a certain symptom or representation. The brain, as a complex organ of multiple entities, processes complex information by connecting multiple brain regions and clarifying the division of labor for each region. This creates a significant obstacle for the research method of brain computer interface, which uses computer algorithms to read information in the brain.  Recently, many solutions and research methods have emerged to address this issue, such as electroencephalography (EEG), Positive Positron Emission Computed Tomography (PET) and Functional Magnetic Resolution Imaging (fMRI), etc. However, with such a large number of technologies, a method that can provide precise data from both temporal and spatial angles has not yet been derived. So in-depth research in biology, continuous exploration of computer algorithms can make outstanding contributions to the refinement of brain computer data. In summary, this article aims to provide an overview of the relationship between emotions and EEG analysis, as well as the principles of machine recognition and other applied technologies.