Decoding human taste perception by reconstructing and mining temporal-spatial features of taste-related EEGs

For humans, taste is essential for perceiving the nutrient content or harmful components of food. The current method of taste sensory evaluation relies on artificial sensory evaluation and an electronic tongue. The former has strong subjectivity and poor repeatability, and the latter is not sufficiently flexible. To decode people's objective taste perception, a strategy for acquiring and recognizing four classes (sour, sweet, bitter, and salty) in taste-related electroencephalograms (EEGs) was proposed. First, according to the proposed experimental paradigm, the taste-related EEGs of subjects under different taste stimulations were collected. Second, to avoid insufficient training of the model due to the small number of EEG samples, a temporal and spatial reconstruction data augmentation (TSRDA) method was proposed, effectively augmenting taste-related EEGs by reconstructing the important features in temporal and spatial dimensions. Third, a multiview channel attention (MVCA) module was introduced into a designed convolutional neural network to extract the important features of the augmented EEG. The proposed method had an accuracy of 99.56