Human-Machine Cooperative Multimodal Learning Method for Cross-subject Olfactory Preference Recognition

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.

Decoding Taste Information in Human Brain: A Temporal and Spatial Reconstruction Data Augmentation Method Coupled with Taste EEG

For humans, taste is essential for perceiving food's nutrient content or harmful components. The current sensory evaluation of taste mainly relies on artificial sensory evaluation and electronic tongue, but the former has strong subjectivity and poor repeatability, and the latter is not flexible enough. This work proposed a strategy for acquiring and recognizing taste electroencephalogram (EEG), aiming to decode people's objective perception of taste through taste EEG. Firstly, according to the proposed experimental paradigm, the taste EEG of subjects under different taste stimulation was collected. Secondly, to avoid insufficient training of the model due to the small number of taste EEG samples, a Temporal and Spatial Reconstruction Data Augmentation (TSRDA) method was proposed, which effectively augmented the taste EEG by reconstructing the taste EEG's important features in temporal and spatial dimensions. Thirdly, a multi-view channel attention module was introduced into a designed convolutional neural network to extract the important features of the augmented taste EEG. The proposed method has accuracy of 99.56%, F1-score of 99.48%, and kappa of 99.38%, proving the method's ability to distinguish the taste EEG evoked by different taste stimuli successfully. In summary, combining TSRDA with taste EEG technology provides an objective and effective method for sensory evaluation of food taste.