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For humans, taste is essential for perceiving food's nutrient content orharmful components. The current sensory evaluation of taste mainly relies onartificial sensory evaluation and electronic tongue, but the former has strongsubjectivity and poor repeatability, and the latter is not flexible enough.This work proposed a strategy for acquiring and recognizing tasteelectroencephalogram (EEG), aiming to decode people's objective perception oftaste through taste EEG. Firstly, according to the proposed experimentalparadigm, the taste EEG of subjects under different taste stimulation wascollected. Secondly, to avoid insufficient training of the model due to thesmall number of taste EEG samples, a Temporal and Spatial Reconstruction DataAugmentation (TSRDA) method was proposed, which effectively augmented the tasteEEG by reconstructing the taste EEG's important features in temporal andspatial dimensions. Thirdly, a multi-view channel attention module wasintroduced into a designed convolutional neural network to extract theimportant features of the augmented taste EEG. The proposed method has accuracyof 99.56%, F1-score of 99.48%, and kappa of 99.38%, proving the method'sability to distinguish the taste EEG evoked by different taste stimulisuccessfully. In summary, combining TSRDA with taste EEG technology provides anobjective and effective method for sensory evaluation of food taste.

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