Research progress on key technologies for emotional recognition of EEG signals
With the continuous progress and widespread application of human-computer interaction technology,accurate identification of user emotions has become increasingly important. Emotion recognition technology has shown great potential in multiple fields,including medical diagnosis,traffic safety,and education. And emotion recognition based on EEG has become a popular research direction in the field of emotion recognition. Firstly, this article introduces the basic concepts of continuous and discrete emotional models, summarizes commonly used EEG public datasets, and compares and analyzes the size of datasets, emotional labels,and their impact on EEG emotion recognition tasks. Secondly,because the impact of different frequency bands and EEG channels on emotion recognition varies, we summarize relevant research on key frequency bands and channels of EEG emotion recognition,and summarize the key frequency band range of EEG emotion recognition and the location of EEG channels with rich emotional information from the literature. Next,we introduce four types of EEG emotional features and provide corresponding feature extraction methods. It also points out the difficulty of extracting various EEG features and their current application effects in emotion recognition. Then, the data augmentation technology and attention mechanism in deep learning based EEG emotion recognition are elaborated,pointing out the mainstream trends of data augmentation technology and the types of artificial emotion features generated. A comparative analysis is conducted on the ways and focuses of various attention mechanisms. Data augmentation technology is used to solve the problem of insufficient EEG data,and attention mechanism plays a key role in improving the accuracy of emotion recognition. Finally, certain prospects are made for the universality of future EEG emotion recognition models and the research direction of EEG acquisition devices.
万方数据
维普
