Fifer Matthew S, Acharya Soumyadipta, Benz Heather L, Mollazadeh Mohsen, Crone Nathan E, Thakor Nitish V
Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
IEEE Pulse. 2012 Jan;3(1):38-42. doi: 10.1109/MPUL.2011.2175636.
One of the most exciting and compelling areas of research and development is building brain machine interfaces (BMIs) for controlling prosthetic limbs. Prosthetic limb technology is advancing rapidly, and the modular prosthetic limb (MPL) of the Johns Hopkins University/ Applied Physics Laboratory (JHU/APL) permits actuation with 17 degrees of freedom in 26 articulating joints. There are many signals from the brain that can be leveraged, including the spiking rates of neurons in the cortex, electrocorticographic (ECoG) signals from the surface of the cortex, and electroencephalographic (EEG) signals from the scalp. Unlike microelectrodes that record spikes, ECoG does not penetrate the cortex and has a higher spatial specificity, signal-to-noise ratio, and bandwidth than EEG signals. We have implemented an ECoG-based system for controlling the MPL in the Johns Hopkins Hospital Epilepsy Monitoring Unit, where patients are implanted with ECoG electrode grids for clinical seizure mapping and asked to perform various recorded finger or grasp movements. We have shown that low-frequency local motor potentials (LMPs) and ECoG power in the high gamma frequency (70,150 Hz) range correlate well with grasping parameters, and they stand out as good candidate features for closed-loop control of the MPL.
研发领域中最令人兴奋且引人注目的领域之一是构建用于控制假肢的脑机接口（BMI）。假肢技术正在迅速发展，约翰·霍普金斯大学/应用物理实验室（JHU/APL）的模块化假肢（MPL）在26个关节中可实现17个自由度的驱动。大脑中有许多信号可供利用，包括皮层中神经元的放电率、来自皮层表面的皮层脑电图（ECoG）信号以及来自头皮的脑电图（EEG）信号。与记录尖峰的微电极不同，ECoG不穿透皮层，并且比EEG信号具有更高的空间特异性、信噪比和带宽。我们在约翰·霍普金斯医院癫痫监测单元实施了一个基于ECoG的系统来控制MPL，在那里患者被植入ECoG电极网格用于临床癫痫发作定位，并被要求执行各种记录的手指或抓握动作。我们已经表明，低频局部运动电位（LMP）和高伽马频率（70 - 150 Hz）范围内的ECoG功率与抓握参数密切相关，并且它们是MPL闭环控制的良好候选特征。
