Abstract
In recent years, more and more researchers have focused on emotion recognition methods based on electroencephalogram (EEG) signals. However, most studies only consider the spatio-temporal characteristics of EEG and the modelling based on this feature, without considering personality factors, let alone studying the potential correlation between different subjects. Considering the particularity of emotions, different individuals may have different subjective responses to the same physical stimulus. Therefore, emotion recognition methods based on EEG signals should tend to be personalized. This paper models the personalized EEG emotion recognition from the macro and micro levels. At the macro level, we use personality characteristics to classify the individuals’ personalities from the perspective of ‘birds of a feather flock together’. At the micro level, we employ deep learning models to extract the spatio-temporal feature information of EEG. To evaluate the effectiveness of our method, we conduct an EEG emotion recognition experiment on the ASCERTAIN dataset. Our experimental results demonstrate that the recognition accuracy of our proposed method is 72.4% and 75.9% on valence and arousal, respectively, which is 10.2% and 9.1% higher than that of no consideration of personalization.
Highlights
Emotion recognition plays an important role in interpersonal communication and human–computer interaction, and the royalsocietypublishing.org/journal/rsos R
Human emotions can be 2 predicted by three methods: non-verbal behaviour methods [1], speech behaviour methods [2], and methods based on physiological signals (such as electroencephalogram (EEG)-based emotion recognition, electrocardiogram-based emotion recognition, etc.)
Among the numerous methods of EEG emotion recognition, it is worth noting that in recent years, the method based on deep learning has achieved a dominant position in improving the performance of EEG emotion recognition
Summary
Emotion recognition plays an important role in interpersonal communication and human–computer interaction, and the royalsocietypublishing.org/journal/rsos R. Because of the complexity of human emotion expression, many emotion recognition methods use physiological signals such as EEG, electrooculogram (EOG), electromyogram (EMG), Galvanic skin response (GSR), respiration and blood pressure etc. Researchers have proposed many methods and models to recognize emotion through EEG signals [11,12,13,14,15]. To consider the influence of personality on emotion, the personality theory widely accepted by scholars is the Big Five personality model proposed by Lew Goldberg in 1990 [21]. They believe that human personality can be described in five dimensions—openness, conscientiousness, extraversion, agreeableness and neuroticism (OCEAN). Winter & Kuiper [25] conducted extensive research on the relationship between personality and emotion in social psychology
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