A Wireless BCI and BMI System for Wearable Robots
提出一套脑电图信号分析方法，包括滤波、去噪、特征提取和分类，通过蓝牙传输控制上肢机器人手臂，并用编码器和陀螺仪实时反馈关节角度和速度。
To increase the performance of a brain-computer interface and brain-machine interface system, we propose some methods and algorithms for electroencephalograph (EEG) signal analysis. The recorded EEG signal is transmitted to the computer and the upper limb robotic arm interface via a bluetooth. To obtain effective commands from brain, the recorded EEG signal is processed by a front filter, denoise filter, feature extraction, and classification, while the personal computer software and upper limb arm are driven by EEG-based commands. Through the encoders and gyroscopes on the upper limb arm, we can acquire some feedback signals in real time, such as joint angle, arm accelerated speed, and angular speed. The theory of wavelet denoising method, common spatial pattern algorithm and linear discriminant analysis algorithm are investigated in this paper. The simulations and experiments demonstrate the effectiveness and accuracy of these algorithms on EEG signal denoising, feature extraction, and classification.