数字农科院2.0

Advancing olfactory perception research with EEG analysis: a dynamic approach of understanding brain responses to almond deterioration

文献类型： 外文期刊

作者： Jianxun Li;Jinhua Han;Shengfan Chen;Bei Li;Lijun Wu;Qianqian Li

作者机构：

关键词： Almond;EEG;Machine learning;Olfactory perception;PSD

期刊名称： Food Chemistry

ISSN： 0308-8146

年卷期： 2025 年 497 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Electroencephalography (EEG) enables the investigation of olfactory perception through neuronal electrical activity. Decoding dynamic oscillatory changes in sensory-cognitive processing is critical to understanding odor-induced brain responses. First, the EEG signals of almond were obtained and transformed into the frequency domain. Welch's method was implemented to extract power spectral density (PSD). Subsequently, the power spectral analysis of brain responses across different regions and frequency bands was investigated. Moreover, the machine learning approach was employed to explore the primary discriminative features. As a result, pronounced oscillatory activity was obtained in delta and alpha bands inducing distinct spatial-frequency responses of increased δ-power in left temporal region and β-power in parieto-occipital region. Critically, the β-band frequencies of 18 Hz and 25.5 Hz, and channels of FP2, FZ, and C3 were confirmed as key features contributing to olfactory analysis. This study provides valuable insights for olfactory perception and applications for quality assessment and storage monitoring.

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