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Olfactory Perception and Neural Rhythms: A Simulation-Based EEG Analysis Using Power Spectral Density

Aadhitya S V1

  • 1Department of Electronics and Communication Engineering, Meenakshi Sundararajan Engineering College, Chennai, India.

Cognitive Neurodynamics
|July 2, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel simulation framework for analyzing olfactory electroencephalography (EEG) signals. The method accurately distinguishes between different smell conditions, paving the way for reproducible olfactory EEG research.

Keywords:
Brain–computer interfaceEEG rhythm analysisMulticlass classificationOlfactory EEGPower spectral densitySimulated EEG dataSupport vector machine

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Signal Processing

Background:

  • Olfactory perception is a key cognitive function, influencing emotions, memory, and decision-making.
  • Studying olfactory brain responses using electroencephalography (EEG) is challenging due to low signal-to-noise ratios and individual variability.
  • A lack of established olfactory EEG databases hinders research progress.

Purpose of the Study:

  • To propose a simulation-based framework for analyzing olfactory EEG signals.
  • To enable researchers to study brain responses to odors using power spectral density (PSD) analysis.
  • To create a reproducible methodological platform for olfactory EEG research.

Main Methods:

  • A simulated olfactory EEG dataset was generated for 50 virtual participants across six olfactory conditions (rose/rotten, low/medium/high concentrations).
  • EEG data (45 channels, 256 Hz sampling rate) underwent band-pass filtering (0.5-70 Hz).
  • Welch's method estimated PSD features for five canonical EEG bands (delta, theta, alpha, beta, gamma), followed by multiclass support vector machine (SVM) classification using Stratified 10-fold cross-validation.

Main Results:

  • The simulation framework successfully generated olfactory EEG data.
  • PSD-based features effectively distinguished between different olfactory conditions.
  • The classification model achieved 99.67% accuracy and a 0.99 macro-averaged F1-score.

Conclusions:

  • The proposed simulation-based framework provides a validated platform for reproducible olfactory EEG studies.
  • This research lays the groundwork for future studies using actual human olfactory EEG data.
  • The findings support the use of PSD analysis and machine learning for olfactory EEG research.