How Machine Learning Will Enable Technologies That Anticipate What The Brain Thinks
This past week, Elon Musk’s new venture Neuralinkmade headlines by showing a video of amonkey playing Pong with his mind, controlled by a surgically implanted wireless device that can directly read brain signals and interpret its intended commands. The technologies that enable such communication between a computer and the brain are called brain-machine interfaces (BMIs).
Brain-machine interfaces – or brain-computer interfaces, the terms are used interchangeably – are technologies designed to directly ‘‘plug’ into the nervous system: the brain, retinas in the eyes (which are actually a part of the brain itself), spinal cord, or peripheral nervous system. The Neuralink example and other similar technologies are designed to read and decode neural signals from individual neurons in selected parts of the brain in an attempt to understand the brain’s outputs. Instead of the outputs going to the arm of a monkey or human controlling a joy stick to play Pong or some other video game, they go to a computer which plays the game instead.
How do they achieve this? Specially designed electrodes are surgically implanted into a target region of the brain where the neural signals need to be recorded. Those signals are then decoded and the intent of the brain interpreted by mathematical models and computer algorithms that take advantage of what is known about how the brain works. Eventually, the commands interpreted by the computer are used to execute desired functions or tasks, such as controlling a robotic arm, generating synthesized speech, or playing video games.
Because surgically implanted BMIs are highly invasive, their use is restricted to restoring clinical function in patients suffering debilitating neurological disorders, in particular motor disorders such as paralysis following spinal cord injury or stroke,locked-in syndrome, andamyotrophic lateral sclerosis(ALS). The impact these technologies can have on the quality of life of these patients and their families cannot be overstated.
Until relatively recently, surgically implantable BMIs necessitated wired connections between the brain and the computer the wires were plugged into. But this has a number of serious disadvantages and risks. The electrodes can move in unintended ways as mechanical forces are exerted on the wires, and it can lead to a significant risk of infection or other types of injury. More recently though, BMIs implanted in the brain have gone wireless. The entire device is self contained within the skull and brain with no external wires protruding out. They communicate with external computers using various ‘‘through the air’ protocols and algorithms in a similar way your Bluetooth and WiFi devices work.
In contrast, non-invasive BMIs are very different from surgically implanted invasive BMIs. Non-invasive BMIs rely onelectroencephalography (EEG) and related methods to read and interpret brain waves. They do not require surgically implanted electrodes, but rather external electrodes integrated into form factors a user can wear and take off as needed – like a cap. The video game industry and virtual and augmented reality worlds have a strong interest in non-invasive BMIs, for example. These market segments are one of the maineconomic driversfor research in this area. Unfortunately though, the resolution and quality of measured brain signals these non-invasive methods provide are generally not sufficient for the needs demanded by clinical applications.
The earliest work using EEG to measure and attempt to make sense of brain signals isover 100 yearsold, dating back to the 1920’s. And the engineering accomplishments behind the press Neuralink has been receiving lately is grounded in years of pioneering work by a number of research groups from around the world.
