  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.
