  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.
