The increasing types of negative emotion impose an extra pressure on the accuracy of emotion recognition

Negative emotion recognition plays a vital role in the field of affective computing. Recent studies reveal fuzzy boundaries among negative emotions that affect emotion recognition performance. However, to the best of our knowledge, there is no formal research to explore the impact of increasing negative emotions on the classification performance of emotion recognition. In this study, a novel dataset was designed and established with three sessions, which respectively include happy, calm fear (session1), session1 + sad (session2), and session 2+angry (session3). All analysis was based on differential entropy (DE) of EEG and heart rate variability (HRV) of ECG. The results show that the similarity of features of different emotions has increased significantly with more and more negative emotions added in the experiment process. This means that the increasing negative emotions video clips in the experimental paradigm affect emotion recognition performance to a great extent. The balanced types of emotional stimuli should be considered in the subsequent design for negative emotion recognition to avoid the adverse impact of increasing negative emotions.