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.

Motor Imagery Classification via ClusteredGroup Sparse Representation A significant limitation on the wide use of a Motor Imagery (MI) BrainComputer Interfaces is the acquisition of electroenchephalogram (EEG) data over a significant amount of EEG trials to achieve accurate classification. In this work, we propose a new sparse representation classification scheme to overcome the above limitation by training the proposed model using only a limited amount of EEG trials. Our algorithm extends current sparse representation classification schemes by exploiting the group sparsity of EEG features. We have evaluated the proposed algorithm on two MI EEG datasets, showing stateoftheart performance against well known classification methods of MI BCI literature.