Building multimodal sensory feedback pathways for SRL with the aim of sensory enhancement via BCI Recently, the Supernumerary Robotic Limbs (SRL) have emerged in the field of robotics for enhancing the useru0027s ability without replacing natural limbs. Lots of researches have contributed to the mechanical design and control of SRL. However, building an artificial sensory nerve pathway of SRL has been largely ignored. In this paper, we built a multimodal sensory feedback pathway for SRL. Three kinds of sensory pathway contained 1) fingertip pressure, 2) slippage and 3) proprioception information are built under the consideration of SRL platform through electrical stimulation. According to the experiment results of eventrelated potential (ERP) amplitude, establish the relationship between the physical parameters of the stimulus and the sensation. In addition, enhancement potentials of human sensation based on the braincomputer interface (BCI) is found in two aspects contained: 1) improvement of subjective perception resolution; 2) Shorten the time of sensory recognition. This paper can be used to objectively benchmark the human subjective and individualized sensation, and guide researchers to build the SRL sensory feedback pathways.

Haptic and Visual Enhancebased Motor Imagery BCI for Rehabilitation LowerLimb Exoskeleton The motor imagery (MI) based on electroencephalogram (EEG) via noninvasive Brain Computer Interface (BCI) is a promising interactive method to provide effective communication between the disabled and rehabilitation robotic device. Interactive efficiency of MIbased BCI largely relies on the performance of subjectxe2x80x99s motor imagery. In this work, we proposed a simple and effective paradigm based on visual and haptic modalities for a highquality initialized and calibrated MIBCI model. Five healthy male subjects participated in motor imagery experiments with two paradigms. The proposed paradigm utilized mixed cues based on the visual and haptic modalities where the haptic cues were provided by the mini vibration motors, and a traditional paradigm utilized the ordinary arrow visual cues. The analysis of the topographic maps was conducted to illustrate the differences of the EEG in both paradigms. The average classification accuracy of motor imagery in the proposed paradigm improved about 14% compared to the traditional paradigm. Furthermore, a subject completed the qualitative verification experiment of the trained model on a rehabilitation lowerlimb exoskeleton which proved the feasibility of the proposed paradigm.