Odor sensory evaluation has a broad application in food, clothing, cosmetics, and other fields. Traditional artificial sensory evaluation has poor repeatability, and the machine olfaction represented by the electronic nose (E-nose) is difficult to reflect human feelings. Olfactory electroencephalogram (EEG) contains odor and individual features associated with human olfactory preference, which has unique advantages in odor sensory evaluation. However, the difficulty of cross-subject olfactory EEG recognition greatly limits its application. It is worth noting that E-nose and olfactory EEG are more advantageous in representing odor information and individual emotions, respectively. In this paper, an E-nose and olfactory EEG multimodal learning method is proposed for cross-subject olfactory preference recognition. Firstly, the olfactory EEG and E-nose multimodal data acquisition and preprocessing paradigms are established. Secondly, a complementary multimodal data mining strategy is proposed to effectively mine the common features of multimodal data representing odor information and the individual features in olfactory EEG representing individual emotional information. Finally, the cross-subject olfactory preference recognition is achieved in 24 subjects by fusing the extracted common and individual features, and the recognition effect is superior to the state-of-the-art recognition methods. Furthermore, the advantages of the proposed method in cross-subject olfactory preference recognition indicate its potential for practical odor evaluation applications.

翻译：气味感官评价在食品、服装、化妆品等领域具有广泛应用。传统人工感官评价可重复性差，以电子鼻为代表的机器嗅觉难以反映人类感受。嗅觉脑电图包含与人类嗅觉偏好相关的气味和个体特征，在气味感官评价中具有独特优势。然而，跨被试嗅觉脑电图识别的困难极大限制了其应用。值得注意的是，电子鼻和嗅觉脑电图在分别表征气味信息和个体情感方面更具优势。本文提出一种基于电子鼻和嗅觉脑电图的多模态学习方法，用于跨被试嗅觉偏好识别。首先，建立嗅觉脑电图与电子鼻多模态数据采集及预处理范式。其次，提出互补性多模态数据挖掘策略，有效挖掘代表气味信息的多模态数据共同特征以及嗅觉脑电图中代表个体情感信息的个体特征。最后，通过融合提取的共同特征与个体特征，在24名被试中实现了跨被试嗅觉偏好识别，且识别效果优于现有最先进方法。此外，本方法在跨被试嗅觉偏好识别中的优势表明其在实际气味评估应用中具有潜力。