Abstract：There are numerous studies measuring the brain emotional status by analyzing EEGs under the emotional stimuli that have occurred, however, in practical application, an important but unresolved question is the extent to which the emotion model may generalize over time, since people could have a different expression of the same physiological signal on different days even when he experiences the same emotion. This paper attempted to add multiple days to the training set in purpose to weaken the impact of day-effect, and then to improve the generalization of the classifier. Eight subjects participated in this experiment, in which movie clips were presented to evoke the subjects’ three emotional states of neutral, positive and negative. Moreover, EEG was recorded 5 times within one month for each subjects. Support vector machine (SVM) was used to obtain the 3-class classification rates in all the collecting conditions including1-day collection, 2-day collection, 3-day collectionand 4-day collection. N-day collection represented the case in which data from N days were sent to train the SVM and the remaining (5-N) days were used to form the testing set. Results showed that the accuracy was increased with the number of days in the training set for most of the subjects. Compared with 1-day collection, the increasing rates of the accuracies were 6.45%(P=0.006), 10.48%(P=0.000), and 14.40%(P=0.000)in 2-day, 3-day and 4-day collections. These results suggested that adding data from more days to the training set could improve the performance and generalization of an emotion classifier. Though it is still a big challenge in EEG-based emotion recognition, these results provided a promising solution and take EEG-based model one step closer to being able to discriminate emotions in practical application.
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