Towards Neurohaptics: Brain-Computer Interfaces for Decoding Intuitive Sense of Touch

TL;DR

This study explores neurohaptics, a brain-computer interface approach that decodes tactile sensations from EEG signals, enabling natural touch and texture perception for applications in virtual reality, robotics, and tactile displays.

Contribution

Introduces a novel neurohaptics system with a new experimental setup and demonstrates the feasibility of decoding actual and imagined touch from EEG signals.

Findings

Successful classification of four different textures from EEG signals

Feasibility of decoding actual touch and touch imagery

Potential for practical neurohaptic applications

Abstract

Noninvasive brain-computer interface (BCI) is widely used to recognize users' intentions. Especially, BCI related to tactile and sensation decoding could provide various effects on many industrial fields such as manufacturing advanced touch displays, controlling robotic devices, and more immersive virtual reality or augmented reality. In this paper, we introduce haptic and sensory perception-based BCI systems called neurohaptics. It is a preliminary study for a variety of scenarios using actual touch and touch imagery paradigms. We designed a novel experimental environment and a device that could acquire brain signals under touching designated materials to generate natural touch and texture sensations. Through the experiment, we collected the electroencephalogram (EEG) signals with respect to four different texture objects. Seven subjects were recruited for the experiment and evaluated classification performances using machine learning and deep learning approaches. Hence, we could confirm the feasibility of decoding actual touch and touch imagery on EEG signals to develop practical neurohaptics.