Trejo Leonard J, Wheeler Kevin R, Jorgensen Charles C, Rosipal Roman, Clanton Sam T, Matthews Bryan, Hibbs Andrew D, Matthews Robert, Krupka Michael
NASA Ames Research Center, Moffett Field, CA 94035, USA.
IEEE Trans Neural Syst Rehabil Eng. 2003 Jun;11(2):199-204. doi: 10.1109/TNSRE.2003.814426.
We are developing electromyographic and electroencephalographic methods, which draw control signals for human-computer interfaces from the human nervous system. We have made progress in four areas: 1) real-time pattern recognition algorithms for decoding sequences of forearm muscle activity associated with control gestures; 2) signal-processing strategies for computer interfaces using electroencephalogram (EEG) signals; 3) a flexible computation framework for neuroelectric interface research; and d) noncontact sensors, which measure electromyogram or EEG signals without resistive contact to the body.
我们正在开发肌电图和脑电图方法，这些方法从人类神经系统中提取用于人机接口的控制信号。我们在四个领域取得了进展：1）用于解码与控制手势相关的前臂肌肉活动序列的实时模式识别算法；2）使用脑电图（EEG）信号的计算机接口的信号处理策略；3）用于神经电接口研究的灵活计算框架；以及4）非接触式传感器，其无需与身体进行电阻接触即可测量肌电图或脑电图信号。
