Adaptive Identification of Food Sweetness Concentration: An Electroencephalogram Feature Classification Network Under Taste Stimulation.

Detecting and identifying consumers' perception of food sweetness can help guide the optimization of food formulations. Electroencephalogram (EEG) detection can capture changes in brain electrical activity in response to different sweet taste stimuli. In this work, we employ EEG detection and propose an EEG Feature Calculation and Classification Network (EFCC-Net) to recognize taste EEG signals under different sweetness concentration stimuli. First, taste-related EEG data from a subject group under varying sweetness concentration stimuli are collected. Then, an EEG Feature Calculation Module (EFCM) is proposed, which utilizes convolutional kernels of different sizes to compute local features from both temporal and spatial dimensions of EEG data. A lightweight self-attention mechanism is employed to compute global features, and a multi-branch computation approach is adopted to enhance feature extraction capability. Next, based on EEG topographic maps, qualitative analysis is conducted to examine differences in brain region activation under varying taste concentrations. Finally, leveraging the proposed EFCM, the EFCC-Net is designed to classify EEG data corresponding to different sweetness levels. Through structural optimization, ablation experiments, and comparisons with state-of-the-art EEG classification methods, EFCC-Net achieves the best classification performance, with an accuracy of 96.57%, a precision of 96.58%, and a recall of 96.53%, while also demonstrating superior stability.