Wang Xiaomin, Zhao Shaokai, Pei Yu, Luo Zhiguo, Xie Liang, Yan Ye, Yin Erwei
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing, China.
Front Hum Neurosci. 2023 Oct 13;17:1180533. doi: 10.3389/fnhum.2023.1180533. eCollection 2023.
Emotion recognition plays a crucial role in affective computing. Recent studies have demonstrated that the fuzzy boundaries among negative emotions make recognition difficult. However, to the best of our knowledge, no formal study has been conducted thus far to explore the effects of increased negative emotion categories on emotion recognition.
A dataset of three sessions containing consistent non-negative emotions and increased types of negative emotions was designed and built which consisted the electroencephalogram (EEG) and the electrocardiogram (ECG) recording of 45 participants.
The results revealed that as negative emotion categories increased, the recognition rates decreased by more than 9%. Further analysis depicted that the discriminative features gradually reduced with an increase in the negative emotion types, particularly in the θ, α, and β frequency bands.
This study provided new insight into the balance of emotion-inducing stimuli materials.
情感识别在情感计算中起着至关重要的作用。最近的研究表明，负面情绪之间模糊的界限使得识别变得困难。然而，据我们所知，迄今为止尚未进行正式研究来探讨增加负面情绪类别对情感识别的影响。
设计并构建了一个包含三个阶段的数据集，其中包含一致的非负面情绪以及增加的负面情绪类型，该数据集由45名参与者的脑电图（EEG）和心电图（ECG）记录组成。
结果显示，随着负面情绪类别的增加，识别率下降了9%以上。进一步分析表明，随着负面情绪类型的增加，判别特征逐渐减少，特别是在θ、α和β频段。
本研究为诱发情感的刺激材料的平衡提供了新的见解。
