适应性识别食物甜度度:一个电脑图特征分类网络在味觉刺激下
He Wang1,2, Hong Men1,3, Yan Shi1,3
1School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China.
Foods (Basel, Switzerland)
|November 27, 2025
概括
这项研究引入了EEG特征计算和分类网络 (EFCC-Net) 来准确识别从大脑信号中感知甜味. 根据EFCC-Net模型,可以在不同甜度度的电脑电图 (EEG) 数据分类方面实现高准确度.
科学领域:
- 神经科学是一个神经科学.
- 食品科学 食品科学 食品科学
- 机器学习 机器学习
背景情况:
- 消费者对食物甜味的看法对于食品配方优化至关重要.
- 电脑电图 (EEG) 信号反映了对味觉刺激的反应中大脑活动的变化.
- 从EEG数据中准确识别味觉仍然是一个挑战.
研究的目的:
- 开发一个新的网络,即EEG特征计算和分类网络 (EFCC-Net),用于识别味道EEG信号.
- 用EEG地形图分析与不同甜度度相关的大脑区域激活模式.
- 为了提高对不同甜度水平的EEG数据分类的准确性和稳定性.
主要方法:
- 从暴露于不同甜度度的人体受试者那里收集了与味道相关的EEG数据.
- 提出了使用卷积内核进行局部特征提取 (时间和空间) 的EEG特征计算模块 (EFCM).
- 实施了用于全球特征计算的轻量级自我注意机制,以及用于增强特征提取的多分支方法,形成了EFCC-Net.
主要成果:
- 埃福克网络实现了高分类性能:96.57%的准确性,96.58%的精度,96.53%的回忆率.
- 对EEG地形图的定性分析揭示了不同味道度的不同大脑区域激活模式.
- 废弃实验和比较证实EFCC-Net的优越性能和稳定性与现有方法相比.
结论:
- 拟议的EFCC网络有效地对与甜味感知相关的EEG信号进行了分类.
- 该EFCM模块增强了从EEG数据中提取相关的时间和空间特征.
- 这种方法为食品科学和神经科学应用中客观评估味觉提供了强大而准确的方法.
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