If the 20th century had the space race, the 21st will be marked by the unravelling of the mystery that is the human mind. But unlike the conquest of space, the race to harness the power of the brain is increasingly being run by private companies, not nations. It’s exciting to witness the tremendous progress happening in the field of neurotechnologies and the broad interest bubbling up around it. While last century’s space race is plastered across the front pages of most streaming apps (e.g. For All Mankind, The Right Stuff, First Man), an equally momentous shift is happening in neuroscience right now, albeit at the sluggish pace of real-time. In the era of binge-watching, the slow trickle of scientific advances and technological breakthroughs can leave one hungry for more, just like the abridged weekly episodes of a favourite show. Good thing, then, that a new episode has aired. The latest instalment in the neurotech saga comes to us courtesy of Kernel, Bryan Johnson’s neural interface company. Let’s press play.
Kernel debuted their first-ever device, Kernel Flow, during a recent live event. Kernel plans to make the Flow available to 50 lucky early partners in the coming months. If that device name sounds familiar, that’s because Kernel had unveiled it earlier this year (which I wrote about here). As a reminder, Kernel had announced two devices, the Flow, which we’ll talk more about in a minute, and the Flux, a magnetoencephalography (MEG) headset (note: I abhor acronyms, but I think we can all agree that a word like that deserves one). Bryan Johnson says the Flux MEG system is a mere 3 to 5 months behind its more mature sibling (Flow), so expect to hear more on that front soon.
A closer look at how Kernel Flow works
The star of the show was the Flow, a time-domain near-infrared spectroscopy (TD-NIRS) system (fine, maybe a few acronyms are okay). As the name suggests, NIRS uses infrared light to measure brain activity. The advantage of using light is that it’s safe (assuming certain limits are respected) and works from outside the body—no surgery required. Specifically, a source (a laser) pressed against the head shines light into the body. The light travels through the various layers (skin, bone, brain, etc), bouncing around like so many pinballs. Some portion of the light is absorbed by the body, while some of it makes it back out after lots of bouncing around and can be measured.
The principle behind NIRS can be demonstrated with a simple experiment most people are familiar with: shining light through the hand with a flashlight. When the light crosses the body, some of it is absorbed and some of it makes it to the other side. That’s why the light looks dimmer after passing through the hand. But why is it red? The answer is that light with a shorter wavelength (towards the purple side of the rainbow) is more strongly absorbed by the body, while the longer wavelengths pass through the body more easily (the red part of the rainbow). Although we cannot see it, infrared also passes through the skin very easily, which is one of the reasons it’s used for these types of measurement. Less absorption means infrared light travels deeper into the body and brings back more valuable information.
While shining light through the hand is an example of transmittance (light goes in on one side, comes out on the other), the human head is too large for that to work. Instead, NIRS relies on reflectance, which is achieved by placing light detectors near the source (e.g. arranged in a circle 1 cm around the laser). These sensors pick up light that bounced around inside the head (was scattered) and came back up to the surface. It’s possible to observe this effect with the same flashlight we shone through our hands earlier. By pressing it against any part of the body, the area around where the flashlight touches the skin will turn red. That’s reflectance.
If NIRS works by shining light into the body and measuring how much of it is reflected, how can it detect brain activity? To answer that, we must first look at an interesting optical property of blood. A major component of blood is hemoglobin, a large protein which binds to oxygen and carries it from the lungs to the rest of the body, where the oxygen is released and used as fuel by cells. Interestingly, hemoglobin absorbs red and infrared light differently depending on whether it’s carrying oxygen or not. And a lot of the body’s absorption of that part of the spectrum comes from hemoglobin. This makes it possible to determine how much oxygenated blood is currently running through a body part by simply looking at how much red and infrared light it absorbs.
This (finally) brings us to the brain. When a region of the brain is active (e.g. because it’s involved in executing some task) the neurons and local tissue consume more oxygen (and glucose). This triggers a relatively fast and specific increase in blood flow to that region, to resupply it with oxygen and other metabolites (this process is called the hemodynamic response). Within seconds, the blood flow to an active region of the brain increases, bringing with it more oxygenated hemoglobin (and more blood volume overall) and “washing out” the deoxygenated hemoglobin. The blood supply goes back to normal within about 15 seconds. When light travels through a region of the brain undergoing this change in blood supply, more infrared light is absorbed because of the higher amount of oxygenated blood in the tissue. This change in absorption is how NIRS indirectly measures brain activity: it simply detects the increase in blood supply caused by the activation of a chunk of brain.
Not to go too far down the rabbit hole here, but there’s one more important piece of information about how Kernel Flow works. So far I’ve described NIRS in general terms, but I began this section by saying that Kernel Flow is a TD-NIRS device. Surely I wouldn’t have added two letters to an acronym (which you know how I feel about by now) just for fun. The TD stands for time-domain: a specific variant of NIRS which relies on extremely short pulses of light (only a few picoseconds long). Because the pulses are much shorter than the time it takes for light to bounce around the body and resurface, we can consider all of the light to have left at the same time. We can then use that assumption to estimate how long any light picked up by the detector travelled for—its time of flight—since we know both when it left the source and when it arrived at the detector. Knowing the time of flight is incredibly valuable because it allows us to “sort” the light based on how long it spent inside the body. Light that came back very quickly—and therefore probably bounced only a few times through the skin or bone and never reached the brain—mostly contains irrelevant information (artifacts). On the other hand, the light that arrived later—and therefore travelled deeper into the brain—contains valuable information about neural activity. Since this “late” light inevitably had to travel through those superficial structures before reaching the brain, we can subtract the signal obtained from the “fast” light to remove most artifacts. This makes TD-NIRS very good at extracting neural information specifically from the brain and ignoring irrelevant parts of the signal from the skin and skull. The additional depth information also helps localize neural activity more precisely within the cortex.
A simulation performed by Kernel showing the likely path (black lines) taken by light arriving at the detector (blue dot) from the source (red dot). Light arriving at the detector quickly (left panels) doesn't travel deep into the tissue, while light arriving later is more likely to have penetrated deep into the tissue (image from Kernel).
There’s one last advantage to TD-NIRS: using two light sources and some smart tricks allows for absolute concentrations of oxygenated and deoxygenation hemoglobin to be measured, instead of just relative changes. We’ll touch on this last bit some more later.
Putting Kernel Flow into context
To understand what Kernel is up against, it’s important to know that despite being available for a long time, NIRS has failed to gain widespread adoption as a tool for neuroscientific research (studying the brains of infants and children being perhaps the only exception, mainly because of how safe and robust to movement NIRS is). It has been steadily growing in popularity but remains a relatively niche brain recording method. While some researcher might push back against this characterization, it’s safe to say that NIRS has not reached as wide of an audience as some of the other non-invasive brain-recording approaches. The main reason for this is that NIRS is often seen as slow, big, imprecise and expensive. Not the best list of adjectives for a brain-computer interface, especially not one that ought to eventually find its way into people’s homes. Kernel is out to change that perception.
When Kernel began looking at this problem about five years ago, they started with a clean slate. They looked at everything under the sun—every possible way to peer into the brain. “We looked at literally everything,” Bryan Johnson tells me. Having quickly concluded that invasive technologies were not going to allow for the rapid democratization of brain interfaces that Kernel wanted to pursue, the team eventually settled on NIRS (and MEG) as the two most promising modalities. They saw great potential in these two somewhat neglected methods for measuring brain activity. In both cases, Kernel foresaw they could tackle many of the challenges preventing wider adoption of these devices, which were mostly engineering, rather than scientific, in nature (i.e. current devices are large, expensive, etc).
Their work clearly paid off. Kernel Flow blows all current TD-NIRS systems out of the water. For instance, a common limitation for NIRS systems is how quickly they can react to incoming light, which is usually measured in photons per second. The best research systems described in the literature reach up to a few tens of millions of photons per second (see here and here for examples). Kernel’s design has already demonstrated 800 million photons per second, with plans to reach above a billion—two orders of magnitude more than the state-of-the-art. When compared to existing commercial devices, the difference is even larger. The Kernel Flow is also a very flexible solution: by relying on a smart modular design, the headgear can be configured with a single sensor or up to 52 for full head coverage. Kernel has yet to announce any pricing, but the Flow is expected to cost an order of magnitude less than comparable devices. While the initial cost is unlikely to put the device in the range of a Christmas gift quite yet, it will be a compelling entry point for researchers, considering what the device offers.
Between the significant technological advances and the benefits inherent to TD-NIRS, Kernel hopes to have pushed the state-of-the-art far enough to unlock a whole new wave of interesting use-cases. Which brings up the following important question.
What exactly are you supposed to do with a TD-NIRS device at home?
The Kernel Flow will undoubtedly offer researchers already working with NIRS a powerful new tool. It will most likely also convince some researchers currently using other brain-measuring devices to make the switch to NIRS. But selling devices to scientists isn’t Kernel’s long term plan. They want to bring neuroscience to the masses. This raises the crucial question: what does one do with a TD-NIRS device at home?
The answer, according to Bryan Johnson, is that they don’t know yet. While this might sound surprising at first, it is the most honest answer he could give. Nobody knows all the ways a product that doesn’t exist yet might be used, especially if that product is a platform for others to build on. The reason that Kernel is creating the tool in the first place is the belief, shared by many in the blossoming neurotech industry, that the use cases will come—once the devices are built. Judging by the backlash Neuralink (another neurotechnology company) received from the scientific community for promising the sun and the moon, one can see why the cautious approach might be more judicious for Kernel.
“Reducing friction,” is what Bryan Johnson tells me Kernel is about. And he has experience doing just that: his previous company, Braintree (which later bought the payment app Venmo), focused on fast, simple and frictionless monetary transactions, to great effect (Braintree was purchased by PayPal for $800 million). The hope is that by building a best-in-class device at an approachable price, the applications will build themselves. At first, Kernel plans to make the Flow available to a wide range of early-adopters interested in exploring the device’s capabilities (neuroscientists, entertainment, gaming and pharmaceutical companies, etc). Once clear and compelling use-cases emerge, Kernel will start distributing the device to consumers.
While Bryan Johnson deliberately withholds any strong opinions about specific “killer apps,” he has some ideas about the broad types of applications Kernel Flow might unlock in the near future. Quantification, he says, is the name of the game. By listening in on the neural symphony reverberating through our brains at any given moment, it might be possible to quantify abstract and often ill-defined concepts, like focus, cognitive load, aging, mental health, pain and a slew of others. This concept of quantification is bolstered by the observation that we humans are remarkably bad at perceiving the world as it really is, especially our own blind spots and biases. Putting numbers to otherwise subjective experiences might bring forth a new era of neuro-quantification and shared understanding, something Bryan Johnson explored in a 2018 post (an interesting read).
At least that’s the vision. In the more immediate future, quantifying brain activity might enable applications more limited in scope, like quantifying “cognitive performance” as it changes throughout the day or based on environmental factors. How did getting only a few hours of sleep last night impact my cognitive performance? What about that heavy meal or long walk? The same idea could be applied to several common activities and situations such as meditation, trying to focus on a task or learning. In the era of Fitbits, Apple Watches and smart scales, one can envision consumers having an appetite for something like that. But it’s hard to imagine how these examples alone could lead to the type of mass adoption Bryan Johnson pictures. After all, Fitbits have a nasty habit of finding their way into the forgotten gadgets drawer.
Another of the Kernel Flow’s strengths, stemming from the TD-NIRS technology it’s based on, is the ability to measure absolute concentrations of oxygenated and deoxygenated hemoglobin. This seemingly small detail enables robust comparisons between measures obtained in different people, different brain regions in a single person or even the same region over time. This is a key advantage over competing recording techniques and might enable interesting new use cases, including health-related ones (prevention, early disease markers, etc). However, as Kernel is well aware, stepping into the medical realm comes with additional delays and countless regulatory hurdles, which can slow down progress and blow up costs. That’s probably why Kernel says they have no immediate plans to seek regulatory approval for their device—but are likely to do so down the road, especially if there is a clear clinical application within reach.
You may have noticed that the list of applications does not include controlling things with your mind, something Neuralink is actively pursuing. Surely using your mind to play video games or control your computer constitute interesting use-cases? The reason those applications aren’t part of the discussion is that NIRS is fundamentally too slow for controlling most things with your mind, where the delay between intention and action needs to be less than about a tenth of a second. As we explored in detail above, NIRS measures an indirect effect of brain activity: changes in blood oxygenation and volume. The biological phenomena that cause these measurable blood changes take time—that’s a fairly inescapable biological reality. It’s like finding out if someone is hungry by waiting for them to eat—you’ll only be able to tell after the fact. Nonetheless, Bryan Johnson believes the naysayers are too quick to dismiss NIRS as fundamentally slow. And there certainly may be ways to speed things up by using smart tricks, especially with a fast device like the Flow. But despite these hypothetical speed gains, it’s doubtful that NIRS could be used for fast-paced input, like playing an online video game. Of course, NIRS would be a good candidate for less time-sensitive control tasks, like turning the lights on. However, as Bryan Johnson points out, direct motor control is unlikely to be the most exciting consumer application for non-invasive brain interfaces. Instead, measuring the state of the brain could make for more interesting scenarios—for instance, rather than simply switching the lights on and off, one might envision a system that automatically adapts the lighting in the room to optimize your focus or alertness.
Could Kernel really get a brain-computer interface in every US home by 2033?
The current Kernel Flow pricing starts at five thousand dollars for a basic configuration. Bryan Johnson estimates that the entry price could drop to as little as a few hundred dollars once it’s produced at high enough volumes. That would put the device well within reach of consumers. And if future iterations bring that price down even further, one can start to see where the 2033 figure comes from.
Given the constraints of a non-invasive device, I would argue that Kernel is doing everything right. They’ve chosen a realistic and unique path, and they’re pouring resources into building a best-in-class device. They’re approaching the broader question of consumer applications with candour, restraint and an open mind. But doing everything right does not guarantee success, and the biggest question mark remains use-cases. It seems to me like quantifying brain performance will not be quite enough to get a majority of people excited. It may be a viable business, sure, but not a device-in-every-home kind of deal.
There are countless cautionary tales of exquisitely engineered technologies failing to convince consumers. Magic Leap and Microsoft built groundbreaking augmented reality headsets, far surpassing what Google glass could do, yet nobody knows quite what to do with them. Or 3D printing, which was lauded as a solution to planned obsolescence and would have people printing their own forks at home by now, yet has so far only found a foothold in specific niches (certainly not as a widespread consumer device).
When I mention Magic Leap, Bryan Johnson answers that unlike augmented reality headsets, a brain-measuring device has inherent value that goes beyond interactive applications and experiences. Accessing the information hidden inside your skull, he argues, is intrinsically valuable—and therefore worth the effort. Even without compelling “entertainment” options, the device might still find other uses, such as providing medically relevant information or supporting positive life changes (better learning, higher focus, etc). While that may be true, I would caution that a functional brain imaging system, like the Flow, provides the most useful information when used within a specific and well-defined context. Just looking at a brain without knowing what it’s doing isn’t typically of much value. Users will presumably need to wear the device while engaged in specific tasks—and will therefore need a good reason to do so. This is not to say that there is no viable path for consumer adoption—but rather that the path has yet to be discovered. What we can say, based on the device’s performance compared to other non-invasive solutions, is that whatever that killer-app ends up being—it will likely run on Kernel’s Flow.
Bryan Johnson spoke of an inflection point, a moment where a particular technology reaches the threshold of “compelling-enough,” starting to make money, unlocking further technological advances and sales in a runaway virtuous cycle. He believes the first iteration of the Flow might push Kernel past that point and put them on an exponential curve of progress and profit. But it’s also possible that their first device, despite its merits, will fall just shy of that threshold. We’ll find out for sure in an upcoming episode. For now, as is regrettably too often the case, we are left with a cliffhanger.
If you enjoyed this story, consider subscribing to my website (you can use this link). That way, you'll automagically be notified every time a new story is online, right in your mailbox! I know, technology, right?