The Neuralink app would guide you through exercises that teach you to control your device.
Simulation.
Not FDA-approved or available.
With a bluetooth connection, you would control any mouse or keyboard, and experience reality — unmediated and in high fidelity.
What is Neuralink developing?
Neuralink is building a fully integrated brain machine interface (BMI) system. Sometimes you`ll see this called a brain computer interface (BCI). Either way, BMIs are technologies that enable a computer or other digital device to communicate directly with the brain. For example, through information readout from the brain, a person with paralysis can control a computer mouse or keyboard. Or, information can be written back into the brain, for example to restore the sense of touch. Our goal is to build a system with at least two orders of magnitude more communication channels (electrodes) than current clinically-approved devices. This system needs to be safe, it must have fully wireless communication through the skin, and it has to be ready for patients to take home and use on their own. Our device, called the Link, will be able to record from 1024 electrodes and is designed to meet these criteria.
What are the biggest challenges in making a scalable BMI?
Neuralink’s technology builds on decades of BMI research in academic labs, including several ongoing studies with human participants. The BMI systems used in these studies have no more than a few hundred electrodes, with connectors that pass through the skin. Also, their use requires laboratory equipment and personnel. Our challenge is to scale up the number of electrodes while also building a safe and effective clinical system that users can take home and operate by themselves. Recent engineering advances in the field and new technologies developed at Neuralink are paving the way for progress on each of the key technical hurdles.
Electrodes
Our Link needs to convert the small electrical signals recorded by each electrode into real-time neural information. Since the neural signals in the brain are small (microvolts), Link must have high-performance signal amplifiers and digitizers. Also, as the number of electrodes increases, these raw digital signals become too much information to upload with low power devices. So scaling our devices requires on-chip, real-time identification and characterization of neural spikes. Our custom chips on the Link meet these goals, while radically reducing per-channel chip size and power consumption over current technology.
The Link needs to be protected from the fluid and salts that bathe surrounding tissue. Making a water-proof enclosure can be hard, but it’s very hard when that enclosure must be constructed from biocompatible materials, replace the skull structurally, and allow over a 1,000 electrical channels to pass through it. To meet this challenge, we are developing innovative techniques to build and seal each major component of the package. For example, by replacing the connection of multiple components with a process that builds them as a single component, we can decrease device size and eliminate a potential failure point.
Our threads are too fine to be manipulated by hand and too flexible to go into the brain on their own (imagine trying to sew a button with thread but no needle). Yet we need to safely insert them with precision and efficiency. Our solution is based on a new kind of surgical robot, whose initial prototype was developed at the University of California. We are innovating on robot design, imaging systems, and software, to build a robot that can precisely and efficiently insert many threads through a single 8 mm skull opening while avoiding blood vessels on the surface of the brain.
Neural spikes contain a lot of information, but that information has to be decoded in order to use it for controlling a computer. Academic labs have designed computer algorithms controlling a virtual computer mouse from the activity of hundreds of neurons. Our devices will be able to connect to over an order of magnitude more neurons. We want to use the additional information for more precise and naturalistic control and to include additional virtual devices such as a keyboard and game controller. To accomplish this, we are building on recent advances in statistics and algorithm design. One challenge is to design adaptive algorithms that maintain reliable and robust performance while continuing to improve over time, including the addition of new capabilities. Ultimately, we want these algorithms to run in real time on our low-power devices.