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EEG-based trial-by-trial texture classification during active touch.

Safaa Eldeeb¹, Douglas Weber², Jordyn Ting²

¹Electrical and Computer Engineering Department, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA. sme46@pitt.edu.

Scientific Reports

|November 28, 2020

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Electroencephalography (EEG) signals can classify textures during active touch. Specific EEG features in mu and beta bands accurately identify roughness, independent of movement type or frequency.

Area of Science:

Neuroscience
Biomedical Engineering
Haptics

Background:

Active touch involves complex sensory processing.
Electroencephalography (EEG) measures brain activity.
Distinguishing sensory features from motor commands is challenging.

Purpose of the Study:

To identify EEG features for texture classification during active touch.
To find features minimally affected by movement type and frequency.
To analyze brain responses to different surface roughness levels.

Main Methods:

Twelve healthy subjects performed rubbing and tapping movements on three textures (smooth, medium rough, rough) at three frequencies (2, 1, 0.5 Hz).
Synchronous EEG and force data were collected.
Support Vector Machine classifiers with tenfold cross-validation were used for classification.

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Main Results:

Total power in mu (8-15 Hz) and beta (16-30 Hz) bands accurately discriminated textures (>84% accuracy).
These EEG features showed low contribution to classifying movement type (<65%) and frequency (<58%).
Systematic feature selection identified salient texture-related EEG signals.

Conclusions:

EEG, particularly mu and beta band power, can reliably classify surface textures during active touch.
Identified EEG features are robust to variations in movement type and frequency.
This research offers insights into the neural basis of tactile texture perception.