Brain-computer interfaces have come a long way quickly. The first successful decoding of vowel sounds from a neural implant was reported in 2009. In 2021, a team from Edward Chang's lab at the University of California, San Francisco decoded the words and sentences of a patient who could not speak.

脑机接口的发展突飞猛进。 2009年，首次有报道称成功从神经植入物中解码出元音。 2021年，加州大学旧金山分校爱德华·张实验室的一个团队解码了一位无法说话的患者的词语和句子。

A new paper by Dr Chang's group, published in Nature Neuroscience, describes the passing of another milestone. He and his colleagues have enabled two severely paralysed people to communicate with them, by means of an avatar, using both words and gestures. That is an auspicious result for the several million individuals around the world paralysed by strokes, traumatic injuries and degenerative illnesses such as motor-neuron disease.

张博士团队发表在《自然·神经科学》上的一篇新论文描述了又一个里程碑的达成。 他和同事让两名重度瘫痪者借助一个虚拟形象，用词语和手势与他们进行交流。 对于全球数百万因中风、创伤性损伤以及运动神经元病等退行性疾病而瘫痪的人来说，这是一个吉祥的成果。

The group did this by implanting, through a small incision in the skull of each patient, an array of sensors that covered a large part of the motor cortex—the brain region responsible for controlling bodily motion. This operation took over two hours, in part because the process for connecting the sensor array to a data-carrying cable is fiddly. But the team are working on a short-cut using a wireless device that would reduce the duration to 40 minutes.

该团队的做法是，通过每位患者颅骨上的一个小切口，植入一组传感器阵列，覆盖运动皮层的大部分区域——运动皮层是负责控制身体运动的大脑区域。 这台手术耗时两个多小时，部分原因是将传感器阵列连接到数据传输线缆的过程十分繁琐。 但团队正在研究一种使用无线设备的捷径，可将手术时间缩短至40分钟。

The implants themselves allowed Dr Chang and his team to collect signals as the patients attempted to say particular words and make particular gestures. They then used those data to train two machine-learning models for each patient—one to decode the words and the other the gestures—allowing them to reconstruct both actions concurrently with approximately two-thirds accuracy. It is the first occasion that such simultaneous decoding has been achieved, allowing patients to communicate more expressively.

植入物本身使张博士及其团队能够在患者尝试说出特定词语、做出特定手势时采集信号。 随后，他们利用这些数据为每位患者训练两个机器学习模型——一个解码词语，另一个解码手势——使他们能够同时重建这两种动作，准确率约为三分之二。 这是首次实现这种同步解码，使患者能够更有表现力地交流。

There is further to go. These two initial individuals were restricted to a vocabulary of about ten words and phrases, such as "hello" and "nice to meet you", and a similar number of gestures, such as a clap or a shrug. But Dr Chang and his collaborators are working on scaling the technology up to handle over 100 actions. Moreover, not only will the sensor array improve the ability to communicate; he also hopes its wide reach across the motor cortex means that it may be possible to monitor actions intended for the whole body. That might permit paralysed individuals to operate devices such as robots and exoskeletons, which really would be life-changing.

还有更长的路要走。 最初这两名患者被限制在约十个词语和短语的词汇量内，如“你好”和“很高兴见到你”，以及数量相近的手势，如拍手或耸肩。 但张博士及其合作者正在努力扩展该技术，以处理100多个动作。 此外，传感器阵列不仅能提升交流能力；他还希望，其在运动皮层上的广泛覆盖意味着或许可以监测意图作用于全身的动作。 这或许能让瘫痪者操作机器人、外骨骼等设备，那才真正会改变生活。