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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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Olfactory Receptors: Location and Structure01:03

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The process of olfaction, also known as the sense of smell, is a sophisticated chemical response system. The specialized sensory neurons that facilitate this process, known as olfactory receptor neurons, are situated in an upper segment of the nasal cavity, known as the olfactory epithelium. Olfactory sensory neurons are bipolar, with their dendrites extending from the epithelium's apex into the mucus that lines the nasal cavity. Airborne molecules, when inhaled, traverse the olfactory...
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Related Experiment Video

Updated: Sep 22, 2025

Author Spotlight: Exploring Olfactory Influences on Corticospinal Excitability - Insights and Innovations in Neurological Research
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Spatial-frequency-temporal convolutional recurrent network for olfactory-enhanced EEG emotion recognition.

Mengxia Xing1, Shiang Hu2, Bing Wei3

  • 1Anhui Province Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei 230601, China; Zhejiang Key Laboratory for Brain-Machine Collaborative Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China.

Journal of Neuroscience Methods
|May 19, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel 3DCNN-BiLSTM model for accurate emotion recognition using electroencephalogram (EEG) signals, achieving up to 98.29% accuracy by integrating olfactory stimuli with video content.

Keywords:
Convolutional recurrent neural networkEEGEmotion recognitionOdor-Video stimulation

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Area of Science:

  • Affective computing
  • Neuroscience
  • Signal processing

Background:

  • Multimedia stimulation is key for emotion induction.
  • Emotion recognition from electroencephalogram (EEG) signals is a growing area in affective computing.

Purpose of the Study:

  • To develop a novel model for EEG-based emotion recognition.
  • To investigate the impact of olfactory stimuli on emotion induction and recognition accuracy.

Main Methods:

  • Collected EEG data from participants exposed to synchronized odor and video stimuli, creating the Odor-Video Physiological Signal Database (OVPD).
  • Developed a 3D Convolutional Neural Network-Bidirectional Long Short-Term Memory (3DCNN-BiLSTM) model to process 4D EEG representations (spatial, frequency, temporal).
  • Utilized CNN for spatial-frequency feature extraction and BiLSTM for temporal dependency analysis.

Main Results:

  • The 3DCNN-BiLSTM model achieved high accuracy in classifying positive, neutral, and negative emotions: 98.29% with olfactory-enhanced videos and 98.03% with traditional videos on the OVPD dataset.
  • The model demonstrated generalizability by performing well on the public SEED EEG emotion dataset.
  • Olfactory-enhanced videos led to superior emotion recognition accuracy compared to traditional videos across different models.

Conclusions:

  • The 3DCNN-BiLSTM model effectively fuses spatial-frequency-temporal EEG features for robust emotion recognition.
  • Olfactory stimuli can enhance emotion induction and improve EEG-based emotion recognition accuracy.
  • Careful selection of olfactory stimuli is crucial, as unrelated odors may decrease recognition accuracy due to attentional distraction.