思想勝于身體:改善腦-機接口

思想勝于身體:改善腦-機接口

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.

當人們遭受使人衰弱的損傷或神經系統(tǒng)疾病時，他們有時會失去執(zhí)行通常被認為是理所當然的任務的能力，比如走路、聽音樂或開車。他們可以想象做某件事，但傷病可能會阻止這種動作的發(fā)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.

腦-機接口系統(tǒng)可以將腦信號轉換成所需的動作，以恢復某些功能，但使用它們可能是一種負擔，因為它們并不總是平穩(wěn)運行，甚至需要重新調整才能完成簡單的任務。

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.

匹茲堡大學(University of Pittsburgh)和卡耐基梅隆大學(Carnegie Mellon University)的研究人員正在借助腦機接口技術，研究大腦在學習任務時是如何工作的。在《自然生物醫(yī)學工程》(Nature Biomedical Engineering)雜志上發(fā)表的一系列論文中，該研究小組正在推動腦-機接口技術的發(fā)展，旨在幫助使用神經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."

“比方說，在工作日，你計劃晚上去雜貨店，”亞倫·巴蒂斯塔說，他是匹茲堡大學斯旺森工程學院的生物工程副教授。“這個計劃整天都在你大腦的某個地方保持著，但可能直到你真正去商店才會到達你的運動皮層。我們正在開發(fā)腦-機接口技術，希望有一天它能在我們的日常生活中發(fā)揮作用。”

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麗·奧比和卡內基梅隆大學的研究人員合作開發(fā)了從大腦到外部設備的直接通路。他們使用比頭發(fā)還小的電極來記錄神經活動，并將其用于控制算法。

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.

該團隊的第一項研究發(fā)表在去年6月的《美國國家科學院院刊》(Proceedings of the National Academy of Sciences)雜志上。

"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.

“當受試者形成運動意圖時，它會導致電極之間的活動模式，我們將這些活動呈現(xiàn)在電腦屏幕上。”該項目的聯(lián)合主管、卡內基梅隆大學神經科學研究所的生物醫(yī)學工程教授史蒂文·蔡斯說:“研究對象會以一種能喚起他們想要的運動的方式來改變他們的神經活動模式。”

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.

在這項新的研究中，研究小組設計了一種技術，通過這種技術，腦-機接口在后臺不斷地自我調整，以確保系統(tǒng)始終處于校準狀態(tài)，隨時可以使用。

"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."

“我們改變了神經活動如何影響光標的移動，這喚起了學習，”皮特的奧比說，他是這項研究的主要作者。“如果我們以某種方式改變這種關系，就需要我們的動物實驗對象產生新的神經活動模式，以再次學會控制光標的移動。這需要他們幾個星期的練習，我們可以觀察他們學習時大腦是如何變化的。”

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.

從某種意義上說，該算法“學習”如何適應神經記錄接口固有的噪聲和不穩(wěn)定性。研究結果表明，人類掌握一項新技能的過程包括產生新的神經活動模式。研究小組最終希望這項技術能用于中風康復的臨床環(huán)境。