Abstract:

For solving the problem how the hemiplegic patients control the upper-limb exoskeleton during the active rehabilitation training, this study proposed an EEG classification method based on single-trial motor imagery. And this method in the real-time control of an upper-limb exoskeleton developed is applied. Aiming at the low noise-signal ratio and large individual differences of EEG, an advanced CSP algorithm for feature extraction is proposed. Combining this algorithm with SVM classifier, the single-trial motor imagery EEG is classified. Then, this method to construct classification models in two different paradigms is used, and evaluated the classification performance of two models. The classification model which had a better performance is applied in the real-time control of an upper-limb exoskeleton, to verify the feasibility of this method. The average accuracy is 87.12%±2.03% across all subjects in real-time control. The results demonstrate that the upper-limb exoskeleton can be controlled accurately based on the proposed method, and this study is provided the theory evidence and practical basis for BCI technology used in the rehabilitation training.

Key words: CSP, EEG, rehabilitation training, upper-limb exoskeleton