Optimizing food taste sensory evaluation through neural network-based taste electroencephalogram channel selection

TL;DR

This paper introduces CAM-Attention, a neural network-based method for selecting EEG channels to improve taste recognition efficiency and accuracy in food sensory evaluation.

Contribution

It presents a novel channel selection approach combining CNN-CSA and Grad-CAM, enhancing taste EEG analysis by reducing computational load and improving recognition performance.

Findings

Effective taste recognition with reduced EEG channels

Significant decrease in computational cost

High accuracy in distinguishing four tastes

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 paper 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. Finally, the CAM-Attention method reduced the computational burden of taste EEG recognition and effectively distinguished the four tastes. In short, it has excellent recognition performance and provides effective technical support for taste sensory evaluation.