Classification of Tactile Perception and Attention on Natural Textures from EEG Signals

TL;DR

This study explores tactile perception and attention classification using EEG signals, introducing a novel touch imagery paradigm and demonstrating high classification accuracy for object recognition based on tactile-related brain activity.

Contribution

The paper presents a new touch imagery paradigm and validates its effectiveness through neurophysiological analysis and machine learning classification.

Findings

Successful differentiation of tactile perception states from EEG signals

High classification performance achieved with basic machine learning algorithms

Preliminary evidence supporting touch imagery as a viable BCI modality

Abstract

Brain-computer interface allows people who have lost their motor skills to control robot limbs based on electroencephalography. Most BCIs are guided only by visual feedback and do not have somatosensory feedback, which is an important component of normal motor behavior. The sense of touch is a very crucial sensory modality, especially in object recognition and manipulation. When manipulating an object, the brain uses empirical information about the tactile properties of the object. In addition, the primary somatosensory cortex is not only involved in processing the sense of touch in our body but also responds to visible contact with other people or inanimate objects. Based on these findings, we conducted a preliminary experiment to confirm the possibility of a novel paradigm called touch imagery. A haptic imagery experiment was conducted on four objects, and through neurophysiological analysis, a comparison analysis was performed with the brain waves of the actual tactile sense. Also, high classification performance was confirmed through the basic machine learning algorithm.