EEG-based texture roughness classification in active tactile exploration with invariant representation learning networks. (May 2021)

• Record Type: Journal Article
• Authors: Özdenizci, Ozan; Eldeeb, Safaa; Demir, Andaç; Erdoğmuş, Deniz; Akçakaya, Murat
• Is Part Of: Biomedical signal processing and control. Volume 67(2021)
• Publication Date: 2021-05
• Subjects: Haptics -- Texture roughness -- Active tactile exploration -- EEG -- Invariant representations -- Neural networks -- Deep learning -- Adversarial learning; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102507
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: We study EEG-based texture roughness classification during active touch. Different active exploration patterns are also reflected in neural activity. Adversarial invariant feature learning networks are proposed for classification. Invariant representations can suppress movement related variability in decoding. Abstract: During daily activities, humans use their hands to grasp surrounding objects and perceive sensory information which are also employed for perceptual and motor goals. Multiple cortical brain regions are known to be responsible for sensory recognition, perception and motor execution during sensorimotor processing. While various research studies particularly focus on the domain of human sensorimotor control, the relation and processing between motor execution and sensory processing is not yet fully understood. Main goal of our work is to discriminate textured surfaces varying in their roughness levels during active tactile exploration using simultaneously recorded electroencephalogram (EEG) data, while minimizing the variance of distinct motor exploration movement patterns. We perform an experimental study with eight healthy participants who were instructed to use the tip of their dominant hand index finger while rubbing or tapping three different textured surfaces with varying levels of roughness. We use an adversarial invariant representation learning neural network architecture that performs EEG-based classification of different textured surfaces, while simultaneously minimizing the discriminability of motor movement conditions (i.e., rub or tap). Results show that the proposed approach can discriminate between three different textured surfaces with accuracies up to 70%, while suppressing movement related variability from learned representations.