Brain-computer interface provides a new communication channel to control external device by directly translating the brain activity into commands. In this article, as the foundation of electroencephalogram-based robot-assisted upper limb rehabilitation therapy, we report on designing a brain-computer interface-based online robot control system which is made up of electroencephalogram amplifier, acquisition and experimental platform, feature extraction algorithm based on discrete wavelet transform and autoregressive model, linear discriminant analysis classifier, robot control board, and Rhino XR-1 robot. The performance of the system has been tested by 30 participants, and satisfactory results are achieved with an average error rate of 8.5%. Moreover, the advantage of the feature extraction method was further validated by the Graz data set for brain-computer interface competition 2003, and an error rate of 10.0% was obtained. This method provides a useful way for the research of brain-computer interface system and lays a foundation for braincomputer interface-based robotic upper extremity rehabilitation therapy.
In this article, a neurorehabilitation system combining robot-aided rehabilitation with motor imagery-based brain-computer interface is presented. Feature extraction and classification algorithm for the motor imagery electroencephalography is implemented under our brain-computer interface research platform. The main hardware platform for functional recovery therapy is the Barrett Whole-Arm Manipulator. The mental imagination of upper limb movements is translated to trigger the Barrett Whole-Arm Manipulator Arm to stretch the affected upper limb to move along the predefined trajectory. A fuzzy proportional-derivative position controller is proposed to control the Whole-Arm Manipulator Arm to perform passive rehabilitation training effectively. A preliminary experiment aimed at testing the proposed system and gaining insight into the potential of motor imagery electroencephalography-triggered robotic therapy is reported.
This paper presents a novel electroencephalogram (EEG)-triggered upper extremity training system. Motor imagery EEG of upper extremity movements is adopted to trigger the Barrett WAM to perform rehabilitation training for patients with stroke. We focus on fully exploring the patient's movement intention and attention from movement imagination EEG and controlling the WAM robot to perform training effectively. A position controller based on fuzzy logic is presented for the rehabilitation system to drive the WAM robot smoothly. Experimental results with seven participants are reported to show the feasibility and effectiveness of the robotic therapy system.
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