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Related Concept Videos

Olfaction01:25

Olfaction

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The sense of smell is achieved through the activities of the olfactory system. It starts when an airborne odorant enters the nasal cavity and reaches olfactory epithelium (OE). The OE is protected by a thin layer of mucus, which also serves the purpose of dissolving more complex compounds into simpler chemical odorants. The size of the OE and the density of sensory neurons varies among species; in humans, the OE is only about 9-10 cm2.
The olfactory receptors are embedded in the cilia of the...
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Physiology of Smell and Olfactory Pathway01:20

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Humans detect odors with the help of specialized cells located in the upper part of the nasal cavity, called olfactory receptor neurons (ORNs). ORNs possess hair-like structures called cilia, which are receptive to sensations from the inhaled air. When an odorant molecule binds to a specific receptor on the cell of the cilia, it leads to a series of events that ultimately cause the ORN to send electrical signals to the olfactory bulb in the brain through the olfactory nerves.
The olfactory...
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Related Experiment Video

Updated: Jan 15, 2026

Author Spotlight: Exploring Olfactory Influences on Corticospinal Excitability - Insights and Innovations in Neurological Research
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Understanding the relationship between rosemary odor and mental workload through deep learning.

Evin Şahin Sadık1, Hamdi Melih Saraoğlu1, Sibel Canbaz Kabay2

  • 1Kütahya Dumlupınar University, Faculty of Engineering Depart. of Electrical Electronics Engineering, Kütahya, Turkey.

Neuroscience
|October 10, 2025
PubMed
Summary

Inhaling rosemary aroma may reduce mental workload, as shown by high accuracy in classifying electroencephalogram (EEG) signals using deep learning. This olfactory stimulation improved test performance and learning scores without manual feature extraction.

Keywords:
ClassificationDeep learningEEGMental workloadNeuropsychological task

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Artificial Intelligence

Background:

  • Mental workload assessment is crucial for optimizing performance and preventing errors.
  • Electroencephalogram (EEG) signals offer a direct measure of brain activity.
  • Traditional EEG analysis often requires manual feature extraction, which can be complex and time-consuming.

Purpose of the Study:

  • To investigate the effect of rosemary aroma on mental workload.
  • To develop and validate deep learning models for classifying mental workload from raw EEG signals.
  • To assess the efficacy of olfactory stimulation in reducing mental workload.

Main Methods:

  • Thirty volunteers performed neuropsychological tests under rosemary aroma exposure and a control condition.
  • Electroencephalogram (EEG) data were recorded during task performance.
  • Deep learning models, including Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), were applied to raw EEG signals for mental workload classification without feature extraction.

Main Results:

  • Volunteers exposed to rosemary odor exhibited reduced error rates and improved test success and learning scores.
  • Deep learning models achieved high accuracy (97.11%) in classifying mental workload under rosemary odor.
  • The study demonstrated successful mental workload classification directly from raw EEG signals using deep learning.

Conclusions:

  • Rosemary odor shows potential in reducing mental workload.
  • Deep learning models can effectively analyze raw EEG signals for mental workload classification, bypassing the need for manual feature engineering.
  • Combining olfactory stimulation with advanced AI techniques offers a novel approach to understanding and managing cognitive states.