Neural Adaptive IntegralSlidingMode Controller with a SSVEPbased BCI for Exoskeletons This paper introduces a robust neural adaptive integral sliding mode controller with a SSVEPbased BCI for exoskeletons. A BCI is used to establish the desired trajectories by analyzing EEG signals. The neural networks are used to approximate nonlinear exoskeletonu0027s dynamic. A sliding mode controller is added to guarantee the global asymptotic stability of the tracking trajectory and the neural network approximations. The controlleru0027s design is based on the hypothesis that only classical properties like boundedness of some parameters are known and all other functions are unknown. The closedloop stability of the system is demonstrated using Lyapunov method. The effectiveness of the proposed approach is tested by an experiment application to rehabilitation context using an upper limb exoskeleton of 2DOF.

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.