目前恐高情绪分类中的生理信号主要涉及脑电、心电、皮电等, 考虑到脑电在采集和处理上的局限性以及多模态信号间的融合问题, 提出一种基于6种外周生理信号的动态加权决策融合算法. 首先, 通过虚拟现实技术诱发被试不同程度的恐高情绪, 同步记录心电、脉搏、肌电、皮电、皮温和呼吸这6种外周生理信号; 其次, 提取信号的统计特征和事件相关特征构建恐高情感数据集; 再次, 根据分类性能、模态和跨模态信息提出一种动态加权决策融合算法, 从而对多模态信号进行有效整合以提高识别精度. 最后, 将实验结果与先前相关研究进行对比, 同时在开源的WESAD情感数据集进行验证. 结论表明, 多模态外周生理信号有助于恐高情绪分类性能的提升, 提出的动态加权决策融合算法显著提升了分类性能和模型鲁棒性.
Currently, the physiological signals in the classification of acrophobia emotions mainly involve electroencephalogram (EEG), electrocardiogram (ECG), and skin electromyography (EMG). However, due to the limitations of EEG acquisition and processing as well as the fusion between multimodal signals, a dynamic weighted decision fusion algorithm based on six peripheral physiological signals is proposed. Firstly, the different levels of acrophobia are induced in the subjects through the virtual reality technology, while six peripheral physiological signals (ECG, BVP, EMG, EDA, SKT, and RESP) are recorded. Secondly, the statistical and event-related features of the signals are extracted to construct a dataset of acrophobia emotions. Thirdly, a dynamic weighted decision fusion algorithm is proposed according to the classification performance, modal, and cross-modal information, so as to effectively integrate multi-modal signals to improve the recognition precision. Finally, the experimental results are compared with previous relevant research, and then verified on the open-source WESAD emotion dataset. The conclusions show that multi-modal peripheral physiological signals are conducive to enhancing the classification performance of acrophobia emotions, and the proposed dynamic weighted decision fusion algorithm significantly improves both the classification performance and model robustness.