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Related Concept Videos

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Gustation is a chemical sense that, along with olfaction (smell), contributes to our perception of taste. It starts with the activation of receptors by chemical compounds (tastants) dissolved in the saliva. The saliva and filiform papillae on the tongue distribute the tastants and increase their exposure to the taste receptors.
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Gustation, or the sense of taste, is intrinsically linked to the anatomical structures located on the tongue. This organ's surface, along with the entirety of the oral cavity, is adorned with stratified squamous epithelium. Evident on the tongue are elevated structures known as papillae (singular = papilla), which house the mechanisms for the transduction of gustatory stimuli. Four distinct types of papillae exist, each identified by their unique morphological attributes: the circumvallate,...
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The perception of a salty flavor is facilitated by sodium ions within the oral salivary fluid. Upon consumption of a salty substance, salt crystals disassemble, leading to the liberation of its constituents—Na+ and Cl- ions. These ions subsequently dissolve into the salivary fluid present in the oral cavity. The external environment of the gustatory cells experiences an elevation in Na+ concentration, thereby establishing a potent concentration gradient. This gradient propels the...
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TasteNet: A novel deep learning approach for EEG-based basic taste perception recognition using CEEMDAN domain

Sagnik De1, Prithwijit Mukherjee1, Anisha Halder Roy1

  • 1Institute of Radio Physics & Electronics, University of Calcutta, Kolkata, 700009, West Bengal, India.

Journal of Neuroscience Methods
|May 2, 2025
PubMed
Summary

This study introduces TasteNet, a deep learning model that accurately recognizes basic tastes from EEG signals with 97.52% accuracy. TasteNet analyzes taste perception by decomposing EEG signals and extracting entropy features for enhanced taste classification.

Keywords:
Att-BiPLSTMCEEMDANCNNEEGEntropy featuresMulti-head attentionTaste perception analysis

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Sensory Science

Background:

  • Taste perception involves the gustatory system detecting chemical stimuli via taste receptors.
  • Analyzing taste perception is crucial for understanding sensory responses and taste disorders.

Purpose of the Study:

  • To develop a deep learning framework for recognizing basic taste stimuli from electroencephalogram (EEG) signals.
  • To enhance the accuracy of taste perception classification using advanced signal processing and neural network architectures.

Main Methods:

  • EEG signals were preprocessed and decomposed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN).
  • Six entropy features (sample, bubble, approximate, dispersion, slope, permutation) were extracted from intrinsic mode functions (IMFs).
  • A novel deep learning model, TasteNet, integrating CNN, multi-head attention, and Att-BiPLSTM was developed.

Main Results:

  • The TasteNet model accurately classified taste stimuli into six categories: no taste, sweet, sour, bitter, umami, and salty.
  • Achieved a high classification accuracy of 97.52 ± 0.48% for taste perception.

Conclusions:

  • TasteNet provides a robust framework for precise taste perception recognition using EEG signals.
  • The combination of CEEMDAN, entropy features, and advanced neural network components (multi-head attention, Att-BiPLSTM) enhances taste sensation identification.