LAUSR

nicate — a fact that many of us feel aware of as we struggle with our smartphone keyboards. For people with severe paralysis, this information bottleneck is much more extreme. Willett et al. report on page 249 the development of a brain–computer interface (BCI) for typing that could eventually let people with paralysis communicate at the speed of their thoughts. Commercially available assistive typing devices predominantly rely on the person using the device being able to make eye movements or deliver voice commands. Eye-tracking keyboards can let people with paralysis type at around 47.5 characters per minute, slower than the 115-per-minute speeds achieved by people without a comparable injury. However, these technologies do not work for people whose paralysis impairs eye movements or vocalization. And the technology has limitations. For instance, it is hard to reread an e-mail, so that you can compose your reply, while you are typing with your eyes. By contrast, BCIs restore function by deciphering patterns of brain activity. Such interfaces have successfully restored simple movements — such as reaching for and manipulating large objects — to people with paralysis. By directly tapping into neural processing, BCIs hold the tantalizing promise of seamlessly restoring function to a wide range of people. But, so far, BCIs for typing have been un able to compete with simpler assistive technologies such as eye-trackers. One reason is that typing is a complex task. In English, we select from 26 letters of the Latin alphabet. Building a classification algorithm to predict which letter a user wants to choose, on the basis of their neural activity, is challenging, so BCIs have solved typing tasks indirectly. For instance, non-invasive BCI spellers present several sequential visual cues to the user and analyse the neural responses to all cues to determine the desired letter. The most successful invasive BCI (iBCI; one that involves implanting an electrode into the brain) for typing allowed users to control a cursor to select keys, and achieved speeds of 40 characters per minute. But these iBCIs, like non-invasive eye-trackers, occupy the user’s visual attention and do not provide notably faster typing speeds. Willett and colleagues developed a different approach, which directly solves the typing task in an iBCI and thereby leapfrogs far beyond past devices, in terms of both performance and functionality. The approach involves decoding letters as users imagine writing at their own pace (Fig. 1). Such an approach required a classification algorithm that predicts which of 26 letters or 5 punctuation marks a user with paralysis is trying to write — a challenging feat when the attempts cannot be observed and occur whenever the user chooses. To overcome this challenge, Willett et al. first repurposed another type of algorithm — a machine-learning algorithm originally developed for speech recognition. This allowed them to estimate, on the basis of neural activity alone, when a user started attempting to write a character. The pattern of neural activity generated each time their study participant imagined a given character was remarkably consistent. From this information, the group produced a labelled data set that contained the neural-activity patterns corresponding to each character. They used this data set to train the classification algorithm. Neuroscience