Neural correlates of perceptual texture change during active touch

Abstract

Introduction: Texture changes occur frequently during real-world haptic explorations, but the neural processes that encode perceptual texture change remain relatively unknown. The present study examines cortical oscillatory changes during transitions between different surface textures during active touch.

Methods: Participants explored two differing textures whilst oscillatory brain activity and finger position data were recorded using 129-channel electroencephalography and a purpose-built touch sensor. These data streams were fused to calculate epochs relative to the time when the moving finger crossed the textural boundary on a 3D-printed sample. Changes in oscillatory band power in alpha (8-12 Hz), beta (16-24 Hz) and theta (4-7 Hz) frequency bands were investigated.

Results: Alpha-band power reduced over bilateral sensorimotor areas during the transition period relative to ongoing texture processing, indicating that alpha-band activity is modulated by perceptual texture change during complex ongoing tactile exploration. Further, reduced beta-band power was observed in central sensorimotor areas when participants transitioned from rough to smooth relative to transitioning from smooth to rough textures, supporting previous research that beta-band activity is mediated by high-frequency vibrotactile cues.

Discussion: The present findings suggest that perceptual texture change is encoded in the brain in alpha-band oscillatory activity whilst completing continuous naturalistic movements across textures.

Keywords: active touch; change detection; electroencephalography; texture perception; time-frequency analysis.

Figure 1

3D printed stimuli 100 mm × 50 mm where left is the smooth portion of the tile and right is the rough portion of the tile. Superior (A) and horizontal view (B).

Figure 2

Time–frequency spectrograms for electrode 35 (A) and cluster one (electrodes 103 and 110) (D), black boxes indicate the time (pre-transition texture processing −600 – −200, and transition processing 0–450 ms) and frequency (8–12Hz) where significant effects were identified. The half violin plots depict the probability distributions of the data in electrode 35 (C) and cluster one (electrodes 103 and 110) (F). The individual dots show data points from each participant. The boxplots indicate the median, upper and lower quartiles, as well as the interquartile range (IQR) between the 25th and 75th percentile, whilst the whiskers represent scores outside of the IQR. Grand average topographic maps for the alpha-band are shown, with electrode 35 (B) and electrodes 110 and 103 (E) locations overlayed.

Figure 3

Time–frequency spectrograms for electrode 55 for smooth to rough transition (A) and rough to smooth transition (B), black boxes indicate the time (0–450 ms) and frequency (16–24Hz) where statistically significant effects were identified. The half violin plots depict the probability distributions of the data in electrode 55 (D). The individual dots show data points from each participant. The boxplots indicate the median, upper and lower quartiles, as well as the IQR between the 25th and 75th percentile, whilst the whiskers represent scores outside of the IQR. Grand average topographic maps for beta-band are shown, with electrode 55 location overlayed (C).