Battelle神经旁路技术将运动恢复到瘫痪的男人的手臂和手

来自俄亥俄州州立大学的神经外科医生韦斯纳医疗中心将微电极阵列植入志愿参与者的左初级电机皮质。阵列使用96个单独的电极来记录神经活动。每秒30,000个样品，电极每100毫秒产生近30,000个样品。

To translate this data into specific hand movements, Battelle engineers needed to extract meaningful features, apply classification algorithms to identify patterns in those features, and map the patterns to the participant’s intended hand movements. The engineers then needed to control 130 channels of a neuromuscular electrical stimulator (NMES) sleeve on the participant’s right arm. Even a seconds-long delay between thought and movement would have made the movement too unnatural, rendering the entire system impractical. As a result, all data processing, classification, and decoding had to be done in real time. To achieve performance approaching natural motion, the system had to update 10 times per second, which meant completing all processing steps in less than 100 milliseconds.

Battelle used MATLAB to develop signal processing and machine learning algorithms and run the algorithms in real time.

The participant was shown a computer-generated virtual hand performing movements such as wrist flexion and extension, thumb flexion and extension, and hand opening and closing, and instructed to think about making the same movements with his own hand.

在MATLAB中工作，该团队开发了算法，分析来自植入电极阵列中的96个通道的数据。使用小波工具箱™，它们执行了小波分解，以隔离控制运动的大脑信号的频率范围。

They performed transforms on the results of the decomposition in MATLAB to calculate mean wavelet power (MWP), reducing the 3000 features captured during each 100 millisecond window for a single channel to a single value.

The resulting 96 MWP values were used as feature vectors for machine learning algorithms that translate the features into individual movements.

该团队使用MATLAB测试了多种机器学习技术，包括判别分析和支持向量机（SVM），在定制SVM上进行优化以进行性能。金宝app

在测试会话期间，团队通过参与者尝试视频中显示的动作培训了SVM。他们使用训练有素的SVM输出来为参与者可以在屏幕上操纵的计算机生成的虚拟手动。相同的SVM输出被缩放并用于控制NMES套筒的130个通道。

虽然参与者移动了他的胳膊和手来执行简单的运动，但所有信号处理，解码和机器学习算法都在桌面计算机上实时在Matlab中运行。

Battelle工程师目前正在使用MATLAB为第二代神经电化系统开发算法，该系统将包含加速度计和其他传感器，以使控制算法能够监测臂的位置并检测疲劳。

• 控制瘫痪的手和恢复。“我们使用MATLAB开发的算法从植入的微电极阵列解码信号并致动NMES套筒，使参与者返回他的手臂和手的基本控制，”战士的主要研究统计学家David Friedenberg说。“在研究结束时，他可以抓住一个瓶子，倒出它的内容物，并将其设置下来，然后拿起搅拌棒并执行搅拌运动。”
• 实时处理性能实现。“我们的算法在Matlab运行的60-70毫秒内执行了所有必要的小波分解，解码和其他处理，”Battelle的研究科学家Nick Annetta说。
• Interdisciplinary collaboration enabled.“我是一个统计学家,尼克是一个电气根据合同r, and lots of other engineers and interns worked on the project,” says Friedenberg. “The whole team was comfortable with MATLAB—it was the language that we all had in common.”