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Multiscale EEG feature fusion for recognizing 3D object shapes through active touch.

Jiahui Jiang1, Zhiling Ren1, Jixuan Wang2

  • 1School of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, No.17 North 2nd Ring East Road, Chang'an District Hebei, 050043, PR China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 26, 2026
PubMed
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This summary is machine-generated.

Researchers developed a new electroencephalography (EEG) framework for active tactile shape perception. This system decodes 3D object shapes with 92.20% accuracy using multi-domain brain features.

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Human-Machine Interaction

Background:

  • Tactile perception is crucial for brain function research and developing tactile sensing technologies.
  • Existing electroencephalography (EEG) studies often use simple stimuli or single neural features, limiting understanding of complex tactile shape processing.
  • A systematic EEG framework for active 3D shape discrimination with multi-domain neural analysis is needed.

Purpose of the Study:

  • To investigate the neural mechanisms of human tactile perception of 3D object shapes during active touch.
  • To develop and validate a multi-domain EEG analysis and feature-fusion framework for decoding tactile shape information.
  • To establish a baseline for tactile-dominant multimodal intelligent interaction research.

Main Methods:

Keywords:
EEG analysisFeature fusionNeural networkSpatial tactile perception

Related Experiment Videos

  • Participants actively grasped objects with varying 3D shapes while EEG signals were recorded.
  • Multi-domain EEG analysis included time-frequency energy, dynamical complexity, and brain functional networks.
  • An attention-enhanced multi-channel convolutional neural network was used for feature-fusion-based decoding.

Main Results:

  • Tactile stimulation presence/absence was identified via alpha and beta band event-related synchronization/desynchronization.
  • Increased object edges correlated with decreased brain complexity during active touch.
  • Brain network efficiency and clustering in the high-alpha band correlated with 3D shape categories.
  • The framework achieved 92.20% accuracy in recognizing seven 3D shapes.

Conclusions:

  • The study presents a systematic EEG framework for active 3D tactile shape perception.
  • Multi-domain neural features provide complementary information for reliable shape decoding.
  • Findings offer insights into neural representations of 3D tactile shape perception.