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FBANet: An Effective Data Mining Method for Food Olfactory EEG Recognition.

Xiuxin Xia, Yan Shi, Pengwei Li

    IEEE Transactions on Neural Networks and Learning Systems
    |May 23, 2023
    PubMed
    Summary

    A new frequency band attention network (FBANet) analyzes olfactory electroencephalogram (EEG) to objectively evaluate food odors. This method effectively distinguishes between different food smells, offering a novel approach to food sensory evaluation.

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    Area of Science:

    • Neuroscience
    • Food Science
    • Artificial Intelligence

    Background:

    • Current food sensory evaluation relies on subjective artificial methods or limited machine perception.
    • Objective and reliable methods are needed to capture human olfactory perception for food analysis.

    Purpose of the Study:

    • To introduce a frequency band attention network (FBANet) for analyzing olfactory electroencephalogram (EEG) signals.
    • To differentiate between various food odors using EEG data.
    • To propose a novel approach for objective food sensory evaluation.

    Main Methods:

    • Designed an olfactory EEG evoked experiment to collect data.
    • Preprocessed olfactory EEG data, including frequency division.
    • Developed FBANet comprising frequency band feature mining and self-attention mechanisms for multiband feature extraction and classification.

    Main Results:

    • FBANet demonstrated superior performance compared to existing advanced models.
    • The network effectively mined information from multiband olfactory EEG data.
    • FBANet successfully distinguished differences among eight distinct food odors.

    Conclusions:

    • FBANet offers an effective method for analyzing olfactory EEG data.
    • This approach provides a new paradigm for objective food sensory evaluation.
    • The study highlights the potential of multiband olfactory EEG analysis in food science.