Classification of Tactile Perception and Attention on Natural Textures from EEG Signals
This work addresses the lack of somatosensory feedback in current BCIs, which is a critical limitation for individuals with motor skill loss controlling robot limbs, by proposing a new paradigm.
This paper explores the feasibility of 'touch imagery' as a novel paradigm for Brain-Computer Interfaces (BCIs), aiming to provide somatosensory feedback. The authors conducted an experiment with four objects, comparing brainwave patterns during imagined touch with actual tactile sensations, and achieved high classification performance using basic machine learning algorithms.
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.