用神经网络选关键脑电通道，提升食物味觉评测效率
Optimizing food taste sensory evaluation through neural network-based taste electroencephalogram channel selection
- 结合注意力机制与梯度加权可视化，自动筛选味觉脑电信号关键通道
- 在四类味道识别中表现优异，降低计算负担
- 适合食品感官评价、脑机接口等需要高效信号处理的场景
味觉诱发的脑电图（EEG）可反映不同的大脑活动模式，用于食品感官评价。然而，多通道脑电数据面临计算成本高、效率低的问题，亟需有效的通道选择方法。本文提出一种名为CAM-Attention的通道选择方法，将卷积神经网络与通道和空间注意力（CNN-CSA）模型与梯度加权类激活映射（Grad-CAM）模型相结合。CNN-CSA通过注意力机制提取脑电数据中的关键特征，Grad-CAM实现特征区域的有效可视化，进而基于可视化结果完成通道选择。实验表明，该方法显著降低了味觉脑电识别的计算负担，并有效区分四种基本味道。结果证明其具有出色的识别性能，为味觉感官评价提供了有力的技术支持。
原文摘要 · Abstract (English)
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.
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