Sam Altman and Elon Musk’s rivalry has grown beyond AI to brain-computer interfaces.
Source: Getty
It is not possible to understand the long-term future of artificial intelligence without understanding brain-computer interfaces.
Why is that? Because brain-computer interfaces (BCI) will play a central role in defining how human intelligence and artificial intelligence fit together in a world with powerful AI.
To most people, brain-computer interfaces sounds like science fiction. But this technology is getting real, quickly. BCI is nearing an inflection point in terms of real-world functionality and adoption. Far-fetched though it may sound, capabilities like telepathy will soon be possible.
The world of BCI can be divided into two main categories: invasive approaches and non-invasive approaches. Invasive approaches to BCI require surgery. They entail putting electronics inside the skull, directly in or on the brain. Non-invasive approaches, on the other hand, rely on sensors that sit outside the skull (say, on headphones or a hat) to interpret and modulate brain activity.
In the first part of this article series, published in October, we dove deep into invasive BCI technologies and startups. In this article, we turn our attention to non-invasive BCI.
Together, BCI and AI will reshape humanity and civilization in the years ahead. Now is the time to start paying serious attention to this technology.
A Cornucopia of Sensors
Before we walk through today’s non-invasive BCI startup landscape, let’s spend a moment exploring the core technologies that make non-invasive BCI possible.
Whenever you use your brain to do anything—think a thought, read a book, speak a sentence, move your arm—detectable physical events take place inside your brain in certain patterns. Specifically, information flows through your brain’s neurons via tiny pulses of electricity: the same basic physical force that powers lightbulbs and kitchen appliances and iPhones. These tiny electrical signals trigger other physical activities in your brain as well, including changes in magnetic fields and blood flow.
These physical changes ultimately represent information. Their patterns encode thoughts, concepts, words, actions. And information that is encoded can be decoded. That is what brain-computer interfaces seek to do.
A number of different non-invasive sensors have been developed in order to both interpret (“read”) and modulate (“write”) the brain’s physical activities in different ways. Each has strengths and weaknesses. In order to understand the field of non-invasive BCI, it is essential to understand these different sensor types (also referred to as “modalities”) and the mechanisms by which they operate.
The world’s oldest brain sensor is the electroencephalogram, or EEG. Invented in 1924 in Germany, EEG today remains the most widely used brain sensor in the world.
EEG directly measures electrical activity from the brain using small electrodes placed on the scalp. (Electrodes are simple devices that can detect electrical activity.) EEG is highly precise from a timing perspective: it can measure neuronal activity with millisecond-level accuracy. It is also inexpensive, portable, safe and easy to use.
EEG’s great weakness is how imprecise it is from a spatial perspective. The brain’s electrical signals get heavily distorted as they pass through the skull and scalp on the way to the EEG’s electrodes, making it difficult to pinpoint exactly where in the brain they originated. This is because the skull, like most bone, is a terrible conductor of electricity.
Relatedly, EEG measurements have poor signal-to-noise ratio because the brain’s tiny electrical pulses can easily be drowned out by many other nearby sources of electrical activity: a jaw clenching, a heart beating, or just ambient electromagnetic interference. Simply blinking your eyes can generate electrical activity that is 10 to 100 times stronger than the electrical signals from your brain.
Extracting sufficiently high-fidelity signal from EEG’s noisy data thus represents a long-standing obstacle to using EEG for BCI technology.
Another non-invasive BCI modality is vastly superior to EEG on these dimensions: magnetoencephalography (MEG).
As you may remember from high school physics, electricity and magnetism are two unified aspects of the same underlying natural phenomenon: electromagnetism. So when a neuron fires and generates a tiny electrical signal, it generates a tiny magnetic field at the same time. EEG measures the electrical signal; MEG measures the associated magnetic field.
Compared to electrical fields, the remarkable thing about magnetic fields is that they pass through the skull and scalp almost completely undistorted. As a result, MEG has far greater spatial resolution and localization accuracy than EEG.
What’s the catch?
Today’s MEG systems are room-sized, requiring a magnetically shielded chamber and cryogenic cooling. They cost millions of dollars. This makes them hopelessly impractical for everyday BCI applications.
But promising research is underway to make MEG systems smaller and cheaper. A newer type of MEG based on optically pumped magnetometers (OPM-MEG) shows great promise: it works at room temperature, is small enough to wear on the head and requires less intensive shielding.
OPM-MEG technology is not yet ready for primetime. But it could become an important new BCI modality in the years ahead, offering higher-fidelity brain data than EEG while still avoiding invasive surgery.
A third non-invasive BCI modality worth mentioning is functional near-infrared spectroscopy, or fNIRS.
Instead of measuring electrical activity like EEG does, or magnetic activity like MEG does, fNIRS measures blood flow. Blood flow increases to neurons when they fire because neurons that are firing require more nutrients. By beaming high-wavelength light through the skull and into the brain, fNIRS sensors can detect changes in blood flow and use those patterns to decode brain activity.
fNIRS is today the second most common non-invasive BCI sensor in the world, behind only EEG. This is thanks in large part to the efforts of Bryan Johnson’s startup Kernel over the past decade. Kernel’s key achievement was to miniaturize fNIRS technology, turning it for the first time into a wearable device that could be commercialized at scale. Like EEG, fNIRS is safe, portable and comparatively cheap. fNIRS is more accurate than EEG in terms of location but less accurate than EEG in terms of timing; the two modalities are thus complementary and often used in tandem.
This brings us to today’s buzziest and most promising non-invasive BCI modality of all: focused ultrasound. We will have much more to say about ultrasound in this article. Read on!
The best way to understand the state of the art in non-invasive BCI—what is possible, what is not possible, where the biggest future opportunities lie—is to explore what today’s leading startups are doing. Let’s dive in.
Reading Minds with EEG
A cohort of stealthy startups believes that humble EEG is poised to transform from a familiar but limited sensor into the dominant approach to BCI.
EEG has many advantages. For decades, though, conventional wisdom has held that EEG’s signal quality is simply too poor to support advanced BCI capabilities.
How convenient, then, that one of modern AI’s great strengths is its superhuman ability to extract latent signal from noisy data.
If you are a hardcore deep learning disciple—a “Bitter Lesson” maximalist—there are good reasons for EEG to be your BCI modality of choice. In one word: scale.
The current era of AI has been defined by the principle of scaling. OpenAI popularized the concept of “scaling laws” in 2020: the idea that AI systems predictably improve as training data, model size and compute resources increase. AI’s dramatic advances in the half-decade since then have resulted, more than anything else, from scaling everything up. The reason that large language models are so astonishingly capable is that we figured out how to train them on more or less all the written text that humanity has ever produced.
If one wanted to take the playbook that has worked so well for generative AI and apply it to understanding the human brain, the key would be to collect as much brain training data as possible. And if one wanted to collect as much brain training data as possible, the best sensor to choose would be obvious: EEG. EEG is, put simply, far more scalable than any other BCI modality.
There are several orders of magnitude more EEG systems in the world today than every other kind of BCI sensor combined. EEG devices can be found in most hospitals in the world; by contrast, there are perhaps a few thousand fNIRS systems and a few hundred MEG systems globally. Basic EEG systems are available for under $1,000.
One young startup that exemplifies this AI-first, scaling-first approach to non-invasive BCI is Conduit. Cofounded by one young Oxford researcher and one young Cambridge researcher, Conduit is collecting as much data as possible as quickly as possible in order to train a large foundation model for the brain. The company says it will have collected over 10,000 total hours of brain recordings from several thousand participants by the end of the year.
While Conduit is focused primarily on collecting EEG data, it supplements this with other non-invasive modalities because the company has found that its AI’s performance improves dramatically when trained on multiple sensor modalities from each user rather than just one.
What use case is Conduit envisioning for its technology?
The company’s goal is—astonishingly—to build a BCI product that can decode users’ thoughts before they have even formulated those thoughts into words. In other words, they are seeking to build thought-to-text AI.
And according to the company, the system is already beginning to work. Conduit’s current AI model produces text outputs that achieve ~45% semantic matches with users’ thoughts, and can do so zero-shot (meaning that the AI system is not fine-tuned on any particular individual ahead of time).
A few specific examples will help make this more concrete.
In one example, when a human participant thought the phrase “the room seemed colder,” the AI generated the phrase “there was a breeze even a gentle gust.” In another example, the participant thought “do you have a favorite app or website” and the AI generated “do you have any favorite robot.”
This technology is not yet ready for primetime. 45% accuracy is not good enough for a mass-market product. And, for now, these results are only possible when users put an unwieldy suite of sensors on their heads. But this level of accuracy is nonetheless remarkable when considering that the task at hand is reading people’s minds. And the company is just getting started. Conduit only began scaling its data collection efforts a few months ago; the company plans to increase its training data corpus by several orders of magnitude moving forward.
Imagine what might become possible—imagine how society might change—if it were possible to communicate nuanced ideas to other people and to computers merely by thinking them.
“The biggest lesson from ML in the last decade has been the importance of scale and data,” said Conduit cofounder Rio Popper. “Noninvasive approaches let us collect a much larger and more diverse dataset than we’d be able to if everyone in our dataset had to get brain surgery first.”
Added her cofounder Clem von Stengel: “We founded Conduit because we realized that people could get things done so much faster if we all thought directly in ideas rather than in words. And we could have a much richer understanding of each other and of the world in general.”
Another interesting young startup pushing the limits of what is possible with EEG is Alljoined.
Alljoined, like Conduit, is taking an AI-first approach to non-invasive BCI and is betting on EEG as the right modality given its scalability and accessibility. While Conduit’s goal is to decode thoughts into language, Alljoined’s initial focus is to decode thoughts into images—that is, to faithfully reproduce an image that a user has in his or her “mind’s eye” based on EEG readings, a task known as image reconstruction.
Alljoined’s CEO/cofounder Jonathan Xu co-authored the seminal MindEye2 paper, which showed that generative AI-based methods could achieve accurate image reconstruction based on only modest amounts of fMRI data. Alljoined set out to extend that work from fMRI to EEG data—and has already had success doing so.
The graphics below show some examples of images that Alljoined’s AI system reconstructed from participants’ EEG data. As you can see, the reconstructed outputs are not fully accurate, but these results represent state-of-the-art performance today. And—as we have observed in so many other fields in AI—it is a safe bet that the system’s performance will continue to improve as training data and compute scale.
The top row represents the image that a human participant looked at, and the bottom row represents the image that Alljoined’s AI system reconstructed based on the participant’s EEG data.
Source: Alljoined
Speaking of training data, last year Alljoined open-sourced the first-ever dataset specifically built for image reconstruction from EEG. The dataset contains EEG data from 8 different participants looking at 10,000 images each. Making this data freely available should serve as a helpful catalyst for the entire field.
While Alljoined’s initial focus has been on image reconstruction, the company is also exploring other application areas. One promising area is sentiment analysis—the ability to accurately and granularly identify the emotion that a user is experiencing in real-time. Decoding sentiments directly from brain data could have significant commercial relevance, for instance in marketing and consumer behavior research, and would be far more high-fidelity than the current status quo of asking individuals to self-report their emotions.
One final EEG startup worth mentioning is Israel-based Hemispheric.
Founded by one of the co-creators of Apple’s FaceID technology, Hemispheric is going all in on the pursuit of scaling laws for EEG. The company is establishing EEG data collection facilities around the world, systematizing and modularizing how these facilities are set up in order to scale as quickly as possible.
The company, which plans to come out of stealth mode in the coming months, has spent years developing a novel model architecture to train a state-of-the-art foundation EEG model. The company recently successfully scaled and trained its first multi-billion-parameter model.
“Some companies are focused on developing improved non-invasive sensors, betting that better hardware will unlock high-precision non-invasive BCI products,” said Hemispheric CEO/cofounder Hagai Lalazar. “We are making the opposite bet: that current non-invasive sensing modalities (EEG, MEG, fNIRS) suffice, and that the breakthrough will come not from better sensing but from better decoding of existing signals. AI is the biggest revolution in the history of algorithms, but so far no one has scaled brain activity data collection and model training for decoding neural data. We believe that a breakthrough in developing AI for decoding the ‘language’ of the brain’s electrical activity is the missing link to making non-invasive BCIs pervasive.”
Zooming out, it is important to note that plenty of uncertainty and skepticism still exist as to whether EEG paired with cutting-edge AI will be able to deliver on the lofty visions outlined here. Many observers are doubtful or downright dismissive of the idea that sufficiently high-signal data can ever be extracted from EEG readings to enable advanced BCI use cases. Much skepticism comes in particular from those who focus on invasive approaches to BCI, those who have witnessed and worked with EEG’s limitations first-hand for decades, and/or those who do not come from the world of deep learning. And some recent research has cast doubt on progress in language decoding from EEG.
The skeptics may prove to be right.
The reality, though, is that no one—not the skeptics, not these AI-first EEG startups, not any BCI or AI expert in the world—knows for sure. No one in the world has yet collected EEG training data at massive scale and trained a large neural network on it and assessed its performance. No one has yet definitively validated or falsified the hypothesis that scaling laws exist for EEG foundation models like they do for large language models.
When OpenAI published the first GPT model in 2018, no one could have conceived of, and no one would have believed, the breathtaking performance gains that would result over the next few years from sheer scaling.
Only time will tell whether scaling will prove anywhere near as productive in the world of BCI as it has for LLMs. If it does, don’t sleep on EEG.
Consumer Wearables for Neuromodulation
From FitBit (acquired by Google for $2.1 billion) to Ōura (recently valued at $11 billion) to Apple Watch (generating well over $10 billion in annual revenue), a number of consumer wearable products have achieved breakout success in recent years.
What do all these consumer wearable products have in common? They measure your personal health metrics, but they cannot change them. They can only “read”; they cannot “write”. (The EEG use cases discussed above all likewise involve only reading, not writing.)
A new generation of consumer wearable companies is building brain-focused products that don’t just monitor your brain state but actively modulate it. If these products work as expected, it’s not hard to imagine that one of them could become the next Ōura.
One intriguing example is Somnee Sleep, a startup that has built a headband to improve the quality of its users’ sleep.
Somnee was co-founded by four of the world’s leading sleep scientists, including UC Berkeley professor Dr. Matthew Walker, author of the influential book Why We Sleep.
No mental activity is more universal or more important than sleep. A consumer product that could significantly improve users’ sleep could unlock a massive market opportunity: as a point of reference, $80 billion is spent on sleeping pills annually.
How does Somnee work?
Somnee’s headband uses EEG and other sensors to track your brain’s activity during sleep, learning its particular sleep patterns and signals using AI. It then sends out personalized electrical pulses that nudge your brainwaves into their optimal rhythms for deeper, more efficient sleep. This neuromodulation technology is known as transcranial electrical stimulation, or tES.
Research shows that Somnee’s consumer headband is four times more effective than melatonin and 1.5 times more effective than sleeping pills like Ambien at improving sleep.
Source: Somnee Sleep
Does it actually work?
Peer-reviewed research suggests that it does.
One recent clinical study showed that Somnee’s product is four times more effective than melatonin and 50% more effective than sleeping pills like Ambien at improving sleep efficiency.
In another study that the company recently completed, Somnee’s headband helped users fall asleep twice as fast, stay asleep more than 30 minutes longer and reduce tossing and turning by one-third.
The National Basketball Association recently announced that it is partnering with Somnee to make the company’s product available to NBA players. Equinox will also soon make Somnee’s headbands available in its gyms and hotels.
Another noteworthy startup in this category is UK-based Flow Neuroscience. Similar to Somnee, Flow’s product is a wearable headband that uses transcranial electrical stimulation to generate gentle personalized electrical pulses that modulate its user’s brain activity. But while Somnee focuses on improving sleep, Flow’s product is designed to combat depression.
Depression affects a key region of the brain called the dorsolateral prefrontal cortex. In depressed individuals, the brain cells in this region become less active. Flow’s headband delivers precisely calibrated electrical stimulation directly to the dorsolateral prefrontal cortex in order to stimulate this region and restore healthy brain cell activity patterns.
Both Somnee and Flow rely on transcranial electrical stimulation (tES). But while Somnee uses transcranial alternating current stimulation (tACS), Flow makes use of transcranial direct current stimulation (tDCS). What’s the difference? In short, direct current products like Flow provide a constant current to the brain that make neurons generally more likely to fire, while alternating current products like Somnee introduce an oscillating pulse that influences the rhythms and frequencies at which neurons fire.
Like Somnee, the efficacy of Flow’s product has been validated in peer-reviewed studies. A large clinical trial published last year in Nature Medicine found that the Flow product is twice as effective at addressing depression as antidepressant drugs. According to the study, 57% of clinically depressed patients who used the Flow product reported that they no longer had depression after 10 weeks. The company reports that, of its total user base of tens of thousands of customers, over 75% see some clinical improvement within three weeks.
Flow describes its product as “electricity as medicine.” It is an apt phrase.
Both Somnee and Flow’s headbands are available online to the general public.
One final startup worth mentioning is Neurode. Neurode’s headband uses electrical stimulation to improve its users’ focus and attention. The product is intended both for individuals with ADHD and for members of the broader population looking to boost their overall cognitive functioning.
While Flow uses tDCS (a constant current) and Somnee uses tACS (a rhythmically oscillating current), Neurode uses transcranial random noise stimulation, or tRNS, which delivers current that fluctuates randomly in both its frequency and amplitude. Emerging research suggests that introducing this random noise can boost signal detection in neural circuits, thus improving learning and focus.
According to the company, 45% of its users experience an increase in focus within the first week of using the product.
An emerging body of clinical research indicates that electrical stimulation of the brain with consumer-grade hardware, like these companies are pursuing, can indeed meaningfully influence brain behavior and individual experience in areas as diverse as sleep, depression and focus.
“These startups are building at the right moment,” added Andrea Coravos, a former Entrepreneur in Residence in the FDA’s Digital Health Unit. “The regulatory infrastructure isn’t playing catch-up. The FDA’s first AI/ML framework came out in 2019, and nearly 1,000 AI-enabled devices have been authorized since. That regulatory foundation is what lets companies move from research to real humans, faster.”
But none of these products have yet won mainstream adoption. Time will tell whether these companies are able to craft product experiences that are delightful enough and go-to-market strategies that are effective enough to turn these devices into mass-market successes.
Focused Ultrasound: The Next Great BCI Paradigm?
If there is one BCI technology that offers the greatest upside potential—one approach that could transcend the existing landscape of solutions (including those discussed in this article) and usher in a new paradigm for neurotechnology—it is focused ultrasound. No area within the world of brain-computer interfaces is generating more buzz and excitement today.
What exactly is focused ultrasound, and why is it so promising?
At a basic level, ultrasound is just a subcategory of sound—that is, waves that travel through particles in air and other materials. Humans can hear sound waves that fall within a certain range of frequencies. Ultrasound waves are simply sound waves with a higher frequency than humans can detect with their ears (>20 kilohertz), but that otherwise behave similarly to audible sound waves.
Ultrasound technology has been used for medical imaging for over 75 years (as anyone who has ever been pregnant or had a pregnant loved one will recall).
Focused ultrasound for the brain is a much newer innovation—one that began to take shape only in the 2010s.
The basic concept of focused ultrasound is to aim and launch many ultrasound waves in a precise sequence such that they all converge at one particular point in the brain. All the individual waves add together at that one focal point, creating enough energy density and mechanical pressure to modulate the neurons in particular ways in that one spot while leaving unaffected the rest of the brain tissue that the waves travel through. (The two simple animated graphics on this page do an excellent job of visualizing this phenomenon, making it intuitive to grasp.)
Focused ultrasound offers several unique and compelling advantages as a BCI modality.
The first is that it is orders of magnitude more precise than any other non-invasive BCI modality. EEG, fNIRS and tES all offer spatial resolution of a few centimeters. Focused ultrasound, by contrast, can target a particular spot in the brain with sub-millimeter precision. It can be thought of as a high precision beam that can be aimed at the exact location in the brain that one wants to target.
Second, focused ultrasound can reach deeper into the brain than any other non-invasive technology.
Because non-invasive sensors sit outside the skull, they generally are only able to access and interact with the outermost layer of the brain, known as the neocortex. The neocortex is the seat of advanced cognition and language, so plenty of useful applications are achievable with sensors that can only reach the neocortex. But many important regions and functions sit deeper inside the brain and are therefore out of reach for EEG, fNIRS, tES and other non-invasive sensors.
Deep brain structures like the thalamus, hypothalamus, hippocampus, basal ganglia and amygdala regulate many of our fundamental drives and functions: emotions, memory, attention, appetite, mood, movement, motivation, cravings. The ability to precisely modulate these deep brain regions could enable powerful new treatments for neuropsychiatric disorders as diverse as Parkinson’s, OCD, depression, Alzheimer’s, epilepsy, anxiety, chronic pain and PTSD—not to mention unlocking cognitive augmentation for the general population.
Up until now, access to these deeper regions could only be achieved via invasive methods that require surgery, like deep brain stimulation (DBS). All non-invasive modalities other than ultrasound—whether electrical, magnetic, optical or infrared—are attenuated by human tissue, which means they can make it only a limited distance before they dissipate. Focused ultrasound, by contrast, is a mechanical wave and as a result can pass through human tissue with very little attenuation. This enables it to travel deep into the brain while maintaining its concentrated focus.
And these possibilities are not just theoretical. Recent research has shown that focused ultrasound can, for instance, meaningfully reduce chronic pain in patients; decrease opioid cravings in participants with serious addictions; and minimize tremors for those who suffer from essential tremor—all of which involve accessing deep brain structures.
One final advantage of ultrasound that sets it apart from every other non-invasive modality: it can both read and write, and it can do both with high resolution. No other individual non-invasive modality can carry out both of these functions. EEG, fNIRS and MEG can read, but they cannot write. Transcranial electrical stimulation can write (though at lower resolution and shallower depth than focused ultrasound), but it cannot read.
The ability to both read and write unlocks the holy grail for brain-computer interfaces: closed loop functionality, whereby one unified system can read and decode ongoing neural activity, then stimulate the brain in selective and personalized ways based on what it reads, then see how the brain responds and adapt in realtime, and so on.
Compared to using one device to sense and a different one to modulate, a single device that can both read and write enables perfect alignment between sensing and stimulation, low latency, straightforward calibration, less hardware complexity, greater space efficiency, lower cost, and ultimately more scalable products.
The startup landscape for ultrasound BCI is nascent but developing at breakneck speed.
Today’s most high-profile focused ultrasound startup is Nudge, which recently announced a $100 million fundraise led by Thrive and Greenoaks.
Nudge CEO/cofounder Fred Ehrsam previously cofounded both Coinbase and Paradigm, two of the most successful organizations in the world of crypto. Nudge thus continues the lineage of billionaires launching BCI startups, following Elon Musk with Neuralink, Bryan Johnson with Kernel, and Sam Altman with Merge Labs (more on Merge below). Nudge’s other cofounder Jeremy Barenholtz previously led product and technology at Neuralink.
Nudge’s mission is to advance the state of the art in focused ultrasound across the full stack of hardware, AI and neuroscience in order to enable precise and powerful non-invasive neuromodulation. The company’s initial focus is on treating neuropsychiatric disorders like addiction, chronic pain and anxiety, but its north star ambition is to enable cognitive augmentation for the general population. Nudge aims to make it possible for anyone to precisely and straightforwardly modulate their mental behavior in areas as broad as learning, memory and sleep.
Nudge’s initial form factor is an ultrasound helmet embedded in an MRI machine. (The MRI is used for the “read” side of things. While ultrasound itself can also be used for high-resolution reading, Nudge’s core focus to start is on advancing the state of the art for focused ultrasound “writing.”)
The company’s product is functional and being used on people nearly every day for research studies. This initial product is not portable and not suitable for consumer use, but Nudge is already working on a smaller architecture intended to be used at home and in everyday life.
Nudge’s initial focused ultrasound device, the Nudge Zero.
Source: Nudge
As the company put it: “Imagine a future where chronic pain can be relieved without opioids, where a patient with PTSD can regulate traumatic recall in real time, where clinicians can image and modulate brain circuits as easily as checking a patient’s heart rate. Imagine a future where focus can be enhanced without caffeine, where learning a new language or skill takes days or weeks, rather than months or years. This future isn’t science fiction, it’s an engineering roadmap. And we’re building it now.”
Another promising startup in this category is Sanmai, led by University of Arizona professor and early focused ultrasound pioneer Jay Sanguinetti. Sanmai’s primary backer is Reid Hoffman, who led the company’s recent $12 million fundraise.
Like Nudge, Sanmai is focused to start on ultrasound’s neuromodulation capabilities (its ability to “write” to the brain) rather than its sensing capabilities (its ability to “read”). Compared to Nudge, Sanmai has a more rigorous clinical focus and is less consumer-oriented.
Sanmai’s transcranial focused ultrasound device is already going through clinical studies and is on track to become the first FDA-approved transcranial focused ultrasound device in the world.
Sanmai’s focused ultrasound device is wearable, with an initial focus on treating Parkinson’s Disease.
Source: Sanmai
The first medical indication that Sanmai is focused on treating is Parkinson’s. 10 million people globally suffer from Parkinson’s, with 90 thousand new cases every year in the U.S. alone, making this an important market opportunity. One of Sanmai’s cofounders Taylor Kuhn published some of the earliest work demonstrating focused ultrasound’s efficacy in treating Parkinson’s.
One thing we have not yet touched on is the topic of how, exactly, focused ultrasound is able to address brain-related conditions like Parkinson’s—that is, what the mechanism is by which the technology works. The short answer is that—as with most things related to the brain—we do not yet understand all the details. But the case of Parkinson’s is fascinating and illuminating to consider.
A buildup of misfolded proteins known as alpha-synuclein within neurons in several deep brain regions is believed to be a key driver of Parkinson’s disease. Targeting these deep brain regions with the concentrated mechanical energy of focused ultrasound has been shown to reduce the toxic accumulation of these alpha-synuclein proteins, thereby potentially helping alleviate Parkinson’s symptoms.
Other conditions that Sanmai plans to address in the near term with focused ultrasound include clinical anxiety, which will entail targeting patients’ amygdalas.
“I first began working on ultrasound neuromodulation nearly 15 years ago,” said Sanmai CEO/cofounder Jay Sanguinetti. “Back then, most people were skeptical that the gentle mechanical energy of low-intensity ultrasound could influence brain activity at all. As a graduate student, I had read the early papers—some almost a century old—and felt there was something real there. Early on I had to fight just to get people to look at the data. Today, the field has matured tremendously. We started Sanmai to create the first purpose-built clinical ultrasound neuromodulation device, uniting rigorous safety standards, AI-assisted individualized targeting, and real-world clinical practicality to give clinicians confidence at the point of care.”
Another cutting-edge startup in this field is Forest Neurotech.
Forest Neurotech is a nonprofit—specifically, a novel type of nonprofit startup called a Focused Research Organization (FRO). FROs are an innovative new funding structure developed to support the pursuit of specific, ambitious scientific milestones that are too big or expensive for a typical academic lab but not yet commercially mature enough for industry. FROs generally have startup-like teams and cultures but are funded philanthropically rather than with venture capital dollars. As a result, Forest is not pursuing commercialization, instead focusing solely on advancing the state of the art in foundational ultrasound technology.
Specifically, Forest has focused on miniaturizing ultrasound hardware, a key step toward making the technology broadly accessible.
And it has had impressive success in doing so. Forest recently announced its first device, the Forest 1 brain-computer interface, which is 1,000 times smaller than conventional ultrasound scanners and smaller than a standard key fob.
Forest Neurotech’s device, the Forest 1, is smaller than a standard key fob, can both read and write using ultrasound, and is designed to be implanted inside a patient’s skull.
Source: Forest Neurotech
Importantly, the Forest 1 device can both read and write using ultrasound, which sets it apart from the Nudge and Sanmai devices. It can generate high-resolution three-dimensional images of the entire brain (up to 20 centimeters deep) based on hemodynamics and can also carry out precise neuromodulation.
The Forest 1 device highlights an important nuance when it comes to ultrasound. To this point, we have been discussing ultrasound as a non-invasive BCI technology that does not require surgery. And indeed, ultrasound can be and often is deployed non-invasively: both Nudge and Sanmai are taking a non-invasive approach to ultrasound.
But Forest’s device is invasive: it requires surgery to cut open a patient’s skull and implant the device inside.
Why is this?
The skull is very challenging for ultrasound to deal with, so there are major advantages to putting the ultrasound device inside the skull.
While ultrasound travels through soft tissue like the brain with little attenuation, the same cannot be said of the skull, which is made of bone and does not propagate ultrasound waves well. The skull reflects some ultrasound waves, absorbs others, and scatters and distorts still others.
Figuring out how to account for the unpredictable ways in which ultrasound waves interact with and are affected by the skull is one of the biggest unsolved engineering challenges facing the field of focused ultrasound. Startups like Nudge and Sanmai are devoting tremendous resources to solving this challenge.
Forest’s solution to this problem is, instead, to simply put its device inside the user’s skull. The advantage to this approach is that it avoids entirely the thorny problem of ultrasound waves traveling through the skull. The disadvantage is that any patient who wants to use Forest’s device has to first get brain surgery. There is no free lunch.
Forest refers to its implantation procedure as “minimally invasive” because, while a patient’s skull must be opened up to put the device inside, the device does not penetrate into the patient’s brain tissue; instead, it sits on top of the brain’s protective dura mater layer. This sets it apart from fully invasive BCI technologies like Neuralink and the Utah array that penetrate into the brain.
FROs are generally designed to be time-bound, with the idea that if the team accomplishes its specific scientific goal, it can then spin out a conventional for-profit startup to commercialize it. So don’t be surprised to see one or more for-profit startups emerge from the Forest Neurotech organization before long.
“Device miniaturization and compute expansion have given us more capable technology across healthcare for years,” said Forest cofounder Will Biederman. “Now, with ultrasound, we have the fidelity, precision, and understanding we need to make the non-invasive BCI dream a reality.”
The final ultrasound BCI startup that we will discuss is the most ambitious and frontier of all: Sam Altman’s Merge Labs.
Merge has still not officially launched, so few details are publicly available about the company. (Don’t be surprised to see this change in the coming days, though!)
Sam Altman will serve as one of the company’s cofounders, with OpenAI reportedly investing a significant amount into the company at an $850 million valuation.
Merge will build on recent breakthroughs in ultrasound technology to read from and write to the human brain. But it aims to push the technology frontier further still: the company’s vision is to combine focused ultrasound with gene editing to enable more powerful BCI capabilities. Yes, you read that right: ultrasound plus gene editing!
How does that work?
In broad strokes, gene editing can make specific populations of neurons in the brain responsive to focused ultrasound in specific ways. This emerging area of science is known as sonogenetics.
First, a special gene can be inserted into the DNA of a particular subset of neurons in the brain via genetic engineering. That special gene can encode for a particular protein that is sensitive to mechanical forces. Because focused ultrasound generates tiny mechanical perturbations, that particular protein in those particular neurons will be responsive to the application of focused ultrasound. Specifically, the protein is often an ion channel that opens or closes on demand when it is targeted with focused ultrasound.
Compared to focused ultrasound without gene editing, the sonogenetic approach enables even more precise and tailored control over brain activity. It makes it possible to target specific neurons and neuron types in the brain while leaving others unaffected: for instance, only excitatory and not inhibitory neurons, or only neurons that express certain receptors, or only certain brain circuits (say, the specific projection pathway associated with some addictive behavior).
The sonogetic approach also makes it possible to more directly define and control the mechanism by which focused ultrasound acts upon the brain’s neurons and therefore what effects it has, based on which new genes and proteins are introduced into the neurons.
One of the pioneers of this nascent field of research is renowned Caltech professor Mikhail Shapiro. In a major win for Merge Labs, Shapiro has reportedly joined the company.
Even within the frontier field of brain-computer interfaces, the approach that Merge Labs is pursuing stands out as the most frontier and “science fiction” of all. Basic scientific questions remain to be solved. It will take a decade or more for this vision to come to fruition—if it works at all.
Silent Speech
One final non-invasive startup category worth discussing is silent speech.
Silent speech is technology that can sense and decode the words that someone is attempting to speak, or is envisioning speaking, even though the person is not saying those words out loud. (For this reason it is also referred to as subvocalization.)
Silent speech differs in a key way from all the other technologies and startups discussed in this article: it does not involve decoding signals directly from the brain. Instead, it focuses on physical signals that are downstream from the brain—in particular, signals from a user’s face and mouth that are associated with the intent to speak.
How does silent speech work? The basic idea is that, when a person attempts to speak, various electrical and muscular mechanisms are set in motion in one’s speech system (e.g., tongue, lips, jaw) even if nothing is ever audibly uttered. These physical mechanisms can be detected and decoded.
There is not yet clear consensus as to the best technological approach to enable silent speech. Different companies are pursuing different modalities, and as a general rule, silent speech startups are highly secretive about the details of their technology. What we can say is this: viable approaches to decoding the physical signatures of attempted or imagined speech from a person’s face include technologies based on biomagnetic, optical and radiofrequency data.
When thinking about silent speech, it is helpful to envision a spectrum of possibilities: from (1) full normal speech, to (2) speech that is whispered but still audible, to (3) speech that is not audible but fully mouthed, to (4) speech that is partially mouthed (e.g., the person’s mouth remains closed but the tongue moves inside the mouth), to (5) “speech” that involves almost no physical movement at all, just the mental conceptualization of forming and uttering words.
All silent speech companies are seeking to develop technology that can decode subvocal speech: that is, up through categories (3) and (4) above. Whether silent speech technology will be able to reliably crack category (5)—often referred to as “imagined speech”—remains to be seen.
Voice has recently exploded in popularity as an efficient, convenient and intuitive interface for the age of AI. The promise of silent speech is the ability to use voice as an interface—to communicate with others, to search the internet, to take notes, to respond to emails, and so forth—but to be able to do so privately and discreetly, no matter where you are, whether in an office or in a crowded cafe or on the subway or walking down the street.
Most companies pursuing silent speech envision embedding the technology in a consumer product like headphones or a Bluetooth-like headset. Including an earpiece of some sort in the product form factor is important because it enables a closed loop of private subvocal input paired with private audio output—e.g., so that a user could not only discreetly query an AI model but also discreetly receive a response.
While several promising startups are currently working on silent speech technology, only one has emerged from stealth to date: MIT spinout AlterEgo. AlterEgo’s 3-minute launch video, released two months ago, is worth a watch to get a concrete sense for the concept of silent speech.
“The current way of interacting with computing and AI is limited to how fast you can tap and type on screens and keyboards,” said AlterEgo CEO/cofounder Arnav Kapur. “For the intelligence age, we need an entirely new interface built from the ground up—something that feels like a natural extension of the human mind. To realize this, we had to invent something completely new.”
Expect to see a few more well-funded and pedigreed silent speech competitors emerge from stealth in 2026.
Speculation abounds that technology giants like Apple and Google are seriously exploring silent speech capabilities as a cornerstone technology for future consumer hardware products. Similarly, rumors have swirled that OpenAI’s forthcoming AI-native consumer device, spearheaded by legendary former Apple designer Jony Ive, will involve silent speech.
As a result, we would not be surprised to see some high-profile M&A in this startup category in the near to medium term.
But silent speech will have to overcome a few obstacles to become a major new paradigm for human-computer interaction.
The first is that widespread adoption of silent speech products would necessitate a significant change in consumer behavior and social norms. How many people today would feel comfortable using a product that required silently mouthing words to themselves while sitting in their office or in a cafe?
A more fundamental risk to silent speech is that other BCI technologies that more directly interface with the brain could leapfrog it and eclipse its capabilities. If it becomes possible to extract high-fidelity language signal directly from the brain—if, say, AI-first EEG or next-generation ultrasound imaging fulfill their potential, as discussed above—why bother with subvocal speech? Silent speech may be more private than audible speech and lower latency than typing, but thoughts are more private and lower latency than all of these.
The fact is that none of these technologies is yet ready for primetime. Each is advancing rapidly and each has tremendous potential, but each might yet hit a low performance ceiling or prove impracticable to productize. Time will tell how fast these technologies improve enough to find their way into products that people use and love.
Conclusion
Throughout human civilization, one of the defining themes of technological progress has been an improvement in the speed, bandwidth and fidelity of communication and information transfer. The invention of writing, Gutenberg’s printing press, the telegraph, the radio, the telephone, the internet—the essence of all of these technological leaps was to enhance humanity’s ability to share information.
In general, when more people are able to communicate more information more efficiently with one another, it leads to all sorts of positive things that would have been impossible to predict ahead of time: advances in science, in health, in productivity, in education, in our understanding of each other and of the universe.
Brain-computer interfaces represent the natural next step in this millennia-long march of technology progress.
Going straight to and from the brain is the most effective way of all for information to move among humans and machines. It eliminates the need for lossy intermediaries, including the intermediary of language itself. Language is, after all, highly lossy compression: consider the delta in richness and nuance between your inner mental experience, in all its detail, and how much of that you can capture in words.
High-performance BCIs will unlock all sorts of wonderful and valuable possibilities. The nearest-term impact will be medical, and these benefits will be profound for millions of people around the world who suffer from neuropsychiatric or mental health conditions of different kinds. But this will be just the beginning. Imagine being able to instantly gain new skills—say, karate or scuba diving or golf—merely by “uploading” them to your brain, directly reinforcing the appropriate neural pathways. Imagine being able to recall and relive any memory with perfect “sensory fidelity.” Imagine being able to reprogram your brain to see or feel things that today’s human brains cannot directly sense: Wi-Fi signals, or radio waves, or the direction “true north.”
More to the point, we are not yet able to even conceive of the most profound transformations and opportunities that BCI will usher in—the same way that people in the fourteenth century could not have conceived of all the ways that printed books would transform society (democracy, the scientific method, the Enlightenment); or people in the 1980s could not have conceived of all the ways that the internet would transform society (Bitcoin, cloud computing, Uber).
In the fullness of time, the proliferation of BCI technology throughout society is inevitable. What remains far from settled, however, is whether the prevailing approach to BCI will be non-invasive, or invasive, or some combination.
There are few areas of technology today in which so many well-informed observers hold with deep conviction such diametrically opposed views about how the field will develop going forward. Some subject matter experts make compelling arguments as to why, based on simple laws of physics, the most sophisticated BCI capabilities will always require direct physical interfacing with the brain and therefore surgery. Others argue, also persuasively, that non-invasive techniques are the inevitable end state for this field given their tremendous advantages in scalability, safety and ease of use; and that it is merely a matter of time before sensing, decoding and modulation technologies improve to the point that even the most advanced BCI applications are achievable non-invasively. Still others believe that focusing directly on the brain itself is not even necessary, and that generationally valuable products like silent speech will be built based on signals downstream of the brain.
In the coming years, these technologies will jump from research labs into all of our lives. Buckle up.