思想胜于身体:改善脑-机接口

思想胜于身体:改善脑-机接口

When people suffer debilitating injuries or illnesses of the nervous system, they sometimes lose the ability to perform tasks normally taken for granted, such as walking, playing music or driving a car. They can imagine doing something, but the injury might block that action from occurring.

当人们遭受使人衰弱的损伤或神经系统疾病时，他们有时会失去执行通常被认为是理所当然的任务的能力，比如走路、听音乐或开车。他们可以想象做某件事，但伤病可能会阻止这种动作的发生。

Brain-computer interface systems exist that can translate brain signals into a desired action to regain some function, but they can be a burden to use because they don't always operate smoothly and need readjustment to complete even simple tasks.

脑-机接口系统可以将脑信号转换成所需的动作，以恢复某些功能，但使用它们可能是一种负担，因为它们并不总是平稳运行，甚至需要重新调整才能完成简单的任务。

Researchers at the University of Pittsburgh and Carnegie Mellon University are working on understanding how the brain works when learning tasks with the help of brain-computer interface technology. In a set of papers, the second of which was published today in Nature Biomedical Engineering, the team is moving the needle forward on brain-computer interface technology intended to help improve the lives of amputee patients who use neural prosthetics.

匹兹堡大学(University of Pittsburgh)和卡耐基梅隆大学(Carnegie Mellon University)的研究人员正在借助脑机接口技术，研究大脑在学习任务时是如何工作的。在《自然生物医学工程》(Nature Biomedical Engineering)杂志上发表的一系列论文中，该研究小组正在推动脑-机接口技术的发展，旨在帮助使用神经假肢的截肢患者改善生活。

"Let's say during your work day, you plan out your evening trip to the grocery store," said Aaron Batista, associate professor of bioengineering in Pitt's Swanson School of Engineering. "That plan is maintained somewhere in your brain throughout the day, but probably doesn't reach your motor cortex until you actually get to the store. We're developing brain-computer interface technologies that will hopefully one day function at the level of our everyday intentions."

“比方说，在工作日，你计划晚上去杂货店，”亚伦·巴蒂斯塔说，他是匹兹堡大学斯旺森工程学院的生物工程副教授。“这个计划整天都在你大脑的某个地方保持着，但可能直到你真正去商店才会到达你的运动皮层。我们正在开发脑-机接口技术，希望有一天它能在我们的日常生活中发挥作用。”

Batista, Pitt postdoctoral research associate Emily Oby and the Carnegie Mellon researchers have collaborated on developing direct pathways from the brain to external devices. They use electrodes smaller than a hair that record neural activity and make it available for control algorithms.

巴蒂斯塔、皮特的博士后研究助理艾米丽·奥比和卡内基梅隆大学的研究人员合作开发了从大脑到外部设备的直接通路。他们使用比头发还小的电极来记录神经活动，并将其用于控制算法。

In the team's first study, published last June in the Proceedings of the National Academy of Sciences, the group examined how the brain changes with the learning of new brain-computer interface skills.

该团队的第一项研究发表在去年6月的《美国国家科学院院刊》(Proceedings of the National Academy of Sciences)杂志上。

"When the subjects form a motor intention, it causes patterns of activity across those electrodes, and we render those as movements on a computer screen. The subjects then alter their neural activity patterns in a manner that evokes the movements that they want," said project co-director Steven Chase, a professor of biomedical engineering at the Neuroscience Institute at Carnegie Mellon.

“当受试者形成运动意图时，它会导致电极之间的活动模式，我们将这些活动呈现在电脑屏幕上。”该项目的联合主管、卡内基梅隆大学神经科学研究所的生物医学工程教授史蒂文·蔡斯说:“研究对象会以一种能唤起他们想要的运动的方式来改变他们的神经活动模式。”

In the new study, the team designed technology whereby the brain-computer interface readjusts itself continually in the background to ensure the system is always in calibration and ready to use.

在这项新的研究中，研究小组设计了一种技术，通过这种技术，脑-机接口在后台不断地自我调整，以确保系统始终处于校准状态，随时可以使用。

"We change how the neural activity affects the movement of the cursor, and this evokes learning," said Pitt's Oby, the study's lead author. "If we changed that relationship in a certain way, it required that our animal subjects produce new patterns of neural activity to learn to control the movement of the cursor again. Doing so took them weeks of practice, and we could watch how the brain changed as they learned."

“我们改变了神经活动如何影响光标的移动，这唤起了学习，”皮特的奥比说，他是这项研究的主要作者。“如果我们以某种方式改变这种关系，就需要我们的动物实验对象产生新的神经活动模式，以再次学会控制光标的移动。这需要他们几个星期的练习，我们可以观察他们学习时大脑是如何变化的。”

In a sense, the algorithm "learns" how to adjust to the noise and instability that is inherent in neural recording interfaces. The findings suggest that the process for humans to master a new skill involves the generation of new neural activity patterns. The team eventually would like this technology to be used in a clinical setting for stroke rehabilitation.

从某种意义上说，该算法“学习”如何适应神经记录接口固有的噪声和不稳定性。研究结果表明，人类掌握一项新技能的过程包括产生新的神经活动模式。研究小组最终希望这项技术能用于中风康复的临床环境。