Optimizing Food Taste Sensory Evaluation Through Neural Network-Based Taste Electroencephalogram Channel Selection

Abstract

The taste electroencephalogram (EEG) evoked by the taste stimulation can reflect different brain patterns and be used in applications such as sensory evaluation of food. However, considering the computational cost and efficiency, EEG data with many channels has to face the critical issue of channel selection. This article proposed a channel selection method called class activation mapping with attention (CAM-Attention). The CAM-Attention method combined a convolutional neural network with channel and spatial attention (CNN-CSA) model with a gradient-weighted class activation mapping (Grad-CAM) model. The CNN-CSA model exploited key features in EEG data by attention mechanism, and the Grad-CAM model effectively realized the visualization of feature regions. Then, channel selection was effectively implemented based on feature regions. Experimental results showed that the proposed CAM-Attention method achieved an accuracy of 97.85% and an ${F}1$ -score of 97.74% when the selected channel number was 12, which were only 0.25% and 0.33% lower, respectively, compared to using all channels. This demonstrates that the CAM-Attention method can significantly reduce computational burden while maintaining excellent classification performance. In short, it has excellent recognition performance and provides effective technical support for taste sensory evaluation.