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

Noninvasive brain-computer interface (BCI) decodes brain signals to understand user intention. Especially, decoding of sensation imagery based on brain signals could provide various industries such as developing advanced touch displays and more immersive virtual reality and augmented reality. This paper introduces a preliminary study to develop a neurohaptic-based BCI system for a variety of scenarios using actual touch and touch imagery paradigms. We designed the experimental environment 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 participated in our experiment and evaluated classification performances using the basic machine learning algorithm and convolutional neural network-based deep learning approaches. Hence, we could confirm the feasibility of decoding actual touch and touch imagery on EEG signals for performing versatile and practical neurohaptic BCI.