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<p>The human ability to smell functions as a critical cognitive function because it enables people to detect their surroundings while experiencing feelings and recalling memories and making choices. Researchers face difficulties when they use electroencephalography (EEG) to study how the brain responds to smells because olfactory brain signals produce low signal-to-noise ratios and different people show different response patterns and researchers lack established olfactory EEG databases for their studies. The study proposes a simulation-based framework which enables researchers to study olfactory EEG signals through power spectral density (PSD) analysis. The research team created a simulated olfactory EEG dataset which simulated the responses of fifty virtual participants who experienced two distinct odor categories of pleasant rose and unpleasant rotten at three different concentration levels of low medium and high to create six separate olfactory conditions. The simulated EEG signals included 45 channels which recorded data at a 256&#xa0;Hz sampling rate. Welch’s method estimated PSD features for five canonical EEG frequency bands which included delta theta alpha beta and gamma after the data underwent band-pass filtering at the 0.5–70&#xa0;Hz range. The researchers used Stratified 10-fold cross-validation to evaluate the band’s characteristics which they had developed as training data for their multiclass support vector machine (SVM) classification model. The PSD-based features demonstrated their ability to distinguish between different olfactory conditions in controlled tests which showed the system’s classification accuracy of 99.67% and macro-averaged F1-score of 0.99. The research provides a methodological validation platform which enables scientists to conduct reproducible olfactory EEG studies through their complete pipeline of interpretation. The proposed framework establishes the essential foundations for subsequent research which will assess and develop these techniques through actual human olfactory EEG data in cognitive neuroscience studies.</p>

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Olfactory Perception and Neural Rhythms: A Simulation-Based EEG Analysis Using Power Spectral Density FeaturesOlfactory perception and neural rhythms: a simulation-based eeg analysis using power spectral density features

  • 期刊论文

  • 发表日期：

    2026年6月30日

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  • AADHITYA S.V

摘要

The human ability to smell functions as a critical cognitive function because it enables people to detect their surroundings while experiencing feelings and recalling memories and making choices. Researchers face difficulties when they use electroencephalography (EEG) to study how the brain responds to smells because olfactory brain signals produce low signal-to-noise ratios and different people show different response patterns and researchers lack established olfactory EEG databases for their studies. The study proposes a simulation-based framework which enables researchers to study olfactory EEG signals through power spectral density (PSD) analysis. The research team created a simulated olfactory EEG dataset which simulated the responses of fifty virtual participants who experienced two distinct odor categories of pleasant rose and unpleasant rotten at three different concentration levels of low medium and high to create six separate olfactory conditions. The simulated EEG signals included 45 channels which recorded data at a 256 Hz sampling rate. Welch’s method estimated PSD features for five canonical EEG frequency bands which included delta theta alpha beta and gamma after the data underwent band-pass filtering at the 0.5–70 Hz range. The researchers used Stratified 10-fold cross-validation to evaluate the band’s characteristics which they had developed as training data for their multiclass support vector machine (SVM) classification model. The PSD-based features demonstrated their ability to distinguish between different olfactory conditions in controlled tests which showed the system’s classification accuracy of 99.67% and macro-averaged F1-score of 0.99. The research provides a methodological validation platform which enables scientists to conduct reproducible olfactory EEG studies through their complete pipeline of interpretation. The proposed framework establishes the essential foundations for subsequent research which will assess and develop these techniques through actual human olfactory EEG data in cognitive neuroscience studies.