Abstract
EEG-based emotion recognition can help achieve more natural human-computer interaction, but the temporal non-stationarity of EEG signals affects the robustness of EEG-based emotion recognition models. Most existing studies use the emotional EEG data collected in the same trial to train and test models, once this kind of model is applied to the data collected at different times of the same subject, its recognition accuracy will decrease significantly. To address the problem of EEG-based cross-day emotion recognition, this paper has constructed a database of emotional EEG signals collected over six days for each subject using the Chinese Affective Video System and self-built video library stimuli materials, and the database is the largest number of days collected for a single subject so far. To study the neural patterns of emotions based on EEG signals cross-day, the brain topography has been analyzed in this paper, which show there is a stable neural pattern of emotions cross-day. Then, Transfer Component Analysis (TCA) algorithm is used to adaptively determine the optimal dimensionality of the TCA transformation and match domains of the best correlated motion features in multiple time domains by using EEG signals from different time (days). The experimental results show that the TCA-based domain adaptation strategy can effectively improve the accuracy of cross-day emotion recognition by 3.55% and 2.34%, respectively, in the classification of joy-sadness and joy-anger emotions. The emotion recognition model and brain topography in this paper, verify that the database can provide a reliable data basis for emotion recognition across different time domains. This EEG database will be open to more researchers to promote the practical application of emotion recognition.
Highlights
Emotions are an important part of human psychological structure, and as the humancomputer interaction technology develops, emotional perception computing [1,2] has established a harmonious human-computer environment by enabling computers to perceive, recognize, understand, express, and adapt to human emotions, and allows computers to have a higher and more comprehensive intelligence, which is an important symbol of the naturalization and intelligibility of human-computer interaction [3,4,5].One of the important prerequisites for conducting emotion research is to elicit objective, stable and reliable emotions
In order to address the problem of emotion recognition based on EEG sign studying emotions cross-day, which allows us to collect sufficient emotional samples for different time domains,ofthis has designed deep neural network studies, and investigate the properties
Database, this paper proposes a strategy based on cross-day EEG data to determine the dimension of Transfer Component Analysis (TCA) transform, which can be used to solve the problem of EEG feature matching in different time domains and effectively improve the performance of emotion recognition cross-day
Summary
Emotions are an important part of human psychological structure, and as the humancomputer interaction technology develops, emotional perception computing [1,2] has established a harmonious human-computer environment by enabling computers to perceive, recognize, understand, express, and adapt to human emotions, and allows computers to have a higher and more comprehensive intelligence, which is an important symbol of the naturalization and intelligibility of human-computer interaction [3,4,5].One of the important prerequisites for conducting emotion research is to elicit objective, stable and reliable emotions. Existing publicly available EEG databases based on emotion video stimuli include DEAP [10], MAHNOB-HCI [11], and SEED [12], etc. Based on the above publicly available EEG datasets, various emotion feature extraction methods have been developed and used to recognize emotions from EEG signals. These feature extraction methods include (1) time-domain features: Non-linear features such as statistical features [13], fractal dimensions [14,15], sample entropy [16], and non-stationary indices [17], Hjorth features [18], and higher-order crossover features [19];
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