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EEG-based texture roughness classification in active tactile exploration with invariant representation learning

Ozan Özdenizci1,2, Safaa Eldeeb3, Andaç Demir1

  • 1Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA.

Biomedical Signal Processing and Control
|April 30, 2021
PubMed
Summary

Researchers developed a new method using electroencephalogram (EEG) to identify textured surfaces by touch. This technique successfully distinguishes roughness levels while minimizing variations from hand movement patterns.

Keywords:
Active tactile explorationAdversarial learningDeep learningEEGHapticsInvariant representationsNeural networksTexture roughness

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Human sensorimotor control involves complex interactions between sensory processing and motor execution.
  • Understanding the precise relationship between tactile perception and motor commands remains a challenge in neuroscience.
  • Existing research often focuses on either sensory or motor aspects independently, leaving the integrated processing less explored.

Purpose of the Study:

  • To discriminate between textured surfaces of varying roughness during active tactile exploration using electroencephalogram (EEG) data.
  • To develop a computational model that can classify surface textures based on neural signals.
  • To minimize the influence of motor movement patterns (rubbing vs. tapping) on texture classification accuracy.

Main Methods:

  • An experimental study was conducted with eight healthy participants exploring three distinct textured surfaces.
  • Participants used their dominant index fingertip to either rub or tap the surfaces.
  • An adversarial invariant representation learning neural network architecture was employed for EEG signal analysis and classification.

Main Results:

  • The proposed neural network architecture achieved up to 70% accuracy in discriminating between three different textured surfaces.
  • The method effectively suppressed variability associated with distinct motor exploration movements (rubbing and tapping).
  • Learned representations successfully captured texture-specific information while being invariant to movement types.

Conclusions:

  • It is possible to classify textured surfaces based on EEG signals recorded during active tactile exploration.
  • Adversarial invariant representation learning offers a promising approach to disentangle sensory and motor information in neural data.
  • This work advances our understanding of sensorimotor processing and has potential applications in areas like robotics and neuroprosthetics.