Tag: TMS

  • Brain-to-Brain Communication: Where the Science of Direct Neural Links Actually Stands

    In 2019, researchers at the University of Washington published a paper in Scientific Reports describing BrainNet—a system that allowed three people, seated in separate rooms with no ability to see, hear, or talk to each other, to collaboratively play a Tetris-like game using only their brain signals. Two “senders” could see the game board and decided whether a falling block needed to be rotated. They communicated their decisions to a “receiver” who couldn’t see the board but controlled the game. No words. No gestures. No screens shared between them. The senders’ decisions were extracted via EEG, transmitted over the internet, and delivered to the receiver’s visual cortex via transcranial magnetic stimulation, where they appeared as flashes of light—phosphenes—that the receiver interpreted as instructions. Five groups of three people tested the system and achieved 81 percent accuracy.

    That’s the headline. Here’s the fine print: the information transmitted was binary. Yes or no. Rotate or don’t rotate. One bit of data per transmission cycle. The senders communicated their decisions by staring at lights flashing at different frequencies—15 hertz for one answer, 17 hertz for the other—which entrained their brain’s electrical output at the corresponding frequency, readable by EEG. The receiver experienced either a flash of light (rotate) or no flash (don’t rotate). The “brain-to-brain communication” was, functionally, a very elaborate way to send the equivalent of one binary digit from one head to another. IEEE Spectrum described an earlier version of this approach as “telepathic Morse code.”

    This is what brain-to-brain communication actually looks like in 2026: technically real, scientifically genuine, and approximately as far from telepathy as a tin-can telephone is from a 5G network.

    What exists

    The field has produced a series of legitimate demonstrations, each constrained by the same fundamental bottleneck: you can get information out of a brain with reasonable resolution using EEG or implanted electrodes, but you can deliver information into a brain noninvasively only through crude channels—magnetic pulses that trigger phosphenes (perceived flashes of light) or vague sensations. The input side is the constraint. Reading a brain is hard. Writing to a brain is harder by orders of magnitude.

    The 2014 Starlab experiment was the first reported human brain-to-brain transmission. A sender in India imagined moving his hands or feet to encode binary data through EEG. The signal was emailed to France, where a TMS device delivered pulses to a blindfolded receiver’s visual cortex, producing phosphenes. The receiver reported the flashes verbally, and the team decoded the message. The transmitted words: “hola” and “ciao.” The transmission rate was approximately two bits per minute. The entire process took over an hour.

    BrainNet in 2019 scaled the architecture to three people and demonstrated something genuinely interesting beyond the binary channel: when the researchers injected noise into one sender’s signal, the receiver learned to preferentially weight the more reliable sender—a trust calibration process that happened entirely through brain-to-brain signals without any conscious strategy. The receiver’s brain was doing signal integration across two noisy sources, the same computation that underlies sensory integration in normal perception.

    Invasive brain-computer interfaces—Neuralink, Synchron, Blackrock Neurotech—are advancing rapidly on the reading side. Neuralink implanted its first human chip in January 2024 under its PRIME study, enabling a paralyzed patient to type and control a cursor through thought alone. Synchron’s Stentrode sits inside a blood vessel near the brain, avoiding open surgery. The PRIME study has a primary completion date of 2026 and full study completion projected for 2031. These systems are brain-to-computer interfaces, not brain-to-brain—they translate neural signals into digital commands for external devices. But they represent the reading infrastructure that any brain-to-brain system would eventually need.

    On the AI-assisted decoding side, researchers at the University of Texas in 2023 used fMRI scans and large language models to decode continuous thought into coherent text—not single words or binary choices but streams of semantic content, capturing the gist of what a person was thinking about during a story or imagined narrative. Meta has developed noninvasive brain-scanning systems paired with AI models that can decode silently spoken words from brain activity. These aren’t brain-to-brain systems, but they’re solving the bandwidth problem on the reading end: extracting richer, more nuanced information from neural signals than EEG-based approaches can achieve.

    What doesn’t exist

    Telepathy—the transmission of complex thoughts, images, emotions, or experiences from one mind to another—is not close. The demonstrations that exist transmit binary decisions through artificial sensory channels. The receiver doesn’t “hear” the sender’s thought. The receiver sees a flash of light and interprets it according to a pre-agreed code. The brain-to-brain interface is a translation chain: thought → EEG signal → digital encoding → internet transmission → TMS pulse → phosphene → interpretation. At every link in that chain, information is lost. What arrives in the receiver’s brain is not a thought. It’s a stimulus—a magnetically induced visual artifact that carries one bit of information about the sender’s decision.

    The gap between this and actual telepathy is not a gap that incremental engineering will close, because the limiting factor isn’t the technology between the brains. It’s the fundamental problem of neural encoding: we don’t know, for any given thought, which specific neural firing patterns represent it, how those patterns vary between individuals, or how to induce a specific firing pattern in a target brain that would be experienced as the same thought. Brains aren’t standardized hardware. The neural code for “rotate the block” in one person’s motor cortex is not the same pattern in another person’s motor cortex. Translating one person’s neural representation into a stimulus that would produce the same internal experience in another person requires a mapping between two unique neural architectures—a problem neuroscience hasn’t solved and isn’t close to solving.

    What BCI companies are building toward is not telepathy but increasingly high-bandwidth brain-to-computer interfaces that could, in principle, be linked: Brain A → computer → Brain B. Neuralink’s implant reads neural signals at thousands of channels. Future implants will read more. AI decoding systems are getting better at extracting semantic content from neural data. But the write side—delivering complex, precise, meaningful information directly into neural tissue in a way that the receiving brain interprets as a coherent experience—remains the unsolved problem. TMS can trigger phosphenes and crude sensory impressions. It cannot implant a sentence, an image, an emotion, or a memory.

    The timeline problem

    Coverage of brain-to-brain communication tends to imply a trajectory: binary transmission today, sentences tomorrow, telepathy eventually. The trajectory is real in the same way that the Wright Brothers’ 12-second flight in 1903 implied commercial aviation—the physics supports the possibility, but the engineering required to get from demonstration to deployment is measured in decades, not years, and the technical obstacles on the write side are qualitatively different from the obstacles on the read side.

    Reading a brain is an information extraction problem: the neural signals are there, and the challenge is building sensors sensitive enough and algorithms smart enough to decode them. This problem is yielding to better hardware and better AI. Writing to a brain is an information implantation problem: you need to induce specific patterns of activity in specific neural populations at specific times, through skull and tissue, without disrupting the brain’s existing activity. Noninvasive methods (TMS, focused ultrasound, transcranial electrical stimulation) affect large regions of cortex with limited spatial precision. Invasive methods (optogenetics, direct electrical stimulation) can target individual neurons but require surgery, gene therapy, or implanted hardware.

    The honest assessment in 2026: brain-to-computer interfaces are advancing on a trajectory that will produce clinically meaningful products for paralysis, communication disorders, and sensory prosthetics within the current decade. Brain-to-brain communication, in the sense of transmitting complex mental content between two people, requires solving the neural write problem at a resolution and precision that current technology can’t achieve and that current neuroscience can’t specify. The demonstrations are real. The extrapolation to telepathy is premature by a margin that is difficult to estimate because the bottleneck isn’t engineering velocity. It’s a scientific knowledge gap about how brains encode experience—a gap that better instruments may close but that no existing roadmap guarantees.

    Neuralink named its first consumer product “Telepathy.” The name is aspirational in the way that calling the first automobile a “teleporter” would have been aspirational. The product lets a paralyzed person control a cursor with their thoughts. That’s extraordinary and useful. It’s not telepathy. The distance between the two is the distance between reading a book and writing one—and in neuroscience, we’re still learning to read.

    We cover brain-to-brain communication alongside spinal cord stimulation, retinal implants, and the full landscape of neural interface technology across our Neuroprosthetics course—including why the hardest problem in connecting two brains isn’t getting the signal out. It’s getting the signal in.

  • Can BCIs Treat Depression? The Science of Neural Stimulation for Mental Health in 2026

    In December 2025, the FDA approved the first at-home brain stimulation device for depression. The Flow FL-100, made by a Swedish company called Flow Neuroscience, is a headset that delivers low-level electrical current to the prefrontal cortex—the part of the brain involved in mood regulation and stress response—for 30 minutes at a time. The clinical trial that earned the approval showed 58 percent of patients reaching remission after 10 weeks. The device will be available by prescription in the United States by mid-2026, at a retail price between $500 and $800. Over 55,000 patients have already used it across Europe, the UK, Switzerland, and Hong Kong.

    That’s the accessible end of the spectrum. At the other end—surgically implanted electrodes delivering personalized, closed-loop electrical stimulation directly to deep brain structures—the science is more dramatic, more preliminary, and considerably more difficult to scale. Both approaches share a foundational premise that would have sounded like science fiction twenty years ago: that depression, at least in some patients, can be treated by altering electrical activity in specific brain circuits rather than flooding the entire brain with neurotransmitter-modifying drugs. The question in 2026 is not whether neural stimulation works for depression. It’s how precisely it needs to work, for whom, and at what cost—financially, surgically, and ethically.

    The spectrum of stimulation

    Neural stimulation for mental health spans a range of invasiveness, precision, and evidence quality. Understanding where each technology sits on that spectrum matters more than any individual headline.

    Transcranial direct current stimulation—tDCS—is the least invasive. A device sends a weak electrical current (typically 1 to 2 milliamps) through electrodes placed on the scalp. The current modulates the excitability of neurons in the targeted region without directly triggering them to fire. The Flow device uses this approach. The evidence base is mixed: some trials show clear benefits over placebo, others find little difference. The FDA approval was based on a 174-participant trial published in Nature Medicine. The effect is real but modest—this is not a cure, it’s a tool, and it works better in some patients than others for reasons that aren’t fully understood.

    Transcranial magnetic stimulation—TMS—uses magnetic pulses to induce electrical currents in specific brain regions. It’s been FDA-approved for treatment-resistant depression since 2008 and is administered in clinics, typically over multiple sessions spanning weeks. Repetitive TMS targeting the left dorsolateral prefrontal cortex has the strongest evidence base among non-invasive brain stimulation approaches. An accelerated protocol called Stanford Neuromodulation Therapy, developed at Stanford and published in 2022, compressed the treatment course into five days of intensive stimulation sessions and achieved remission rates approaching 80 percent in a small trial of treatment-resistant patients. The protocol uses brain imaging to personalize the stimulation target for each patient—a significant departure from one-size-fits-all approaches.

    Vagus nerve stimulation—VNS—involves surgically implanting a device that electrically stimulates the vagus nerve in the neck, which sends signals to brain regions involved in mood regulation. It’s been FDA-approved as an adjunctive treatment for treatment-resistant depression since 2005. Response rates are modest and build slowly over months to years. Non-invasive vagus nerve stimulation devices, which stimulate the nerve through the skin of the ear or neck, are being investigated but lack the same evidence base.

    Deep brain stimulation—DBS—is the most invasive: surgeons implant electrodes directly into specific brain structures and deliver electrical impulses through a battery-powered device implanted in the chest. DBS is FDA-approved and well-established for Parkinson’s disease, with over 12,000 patients receiving the treatment annually. For depression, it remains experimental—and the history of DBS for depression is one of the most instructive stories in psychiatric neuroscience about the distance between a promising concept and a working treatment.

    The DBS depression story

    The modern era of DBS for depression began in the early 2000s, when neurologist Helen Mayberg identified a brain region called the subcallosal cingulate—also known as Brodmann area 25—as a key node in the neural circuits underlying depression. In a landmark 2005 study, Mayberg and colleagues implanted DBS electrodes targeting this region in six patients with severe, treatment-resistant depression. Four of six experienced sustained remission. The results were dramatic enough to generate enormous excitement and multiple larger clinical trials.

    Those trials, conducted through the late 2000s and 2010s, produced highly variable results. A major randomized controlled trial sponsored by St. Jude Medical (now Abbott) was halted in 2013 after a futility analysis suggested the treatment was unlikely to show significant benefit over sham stimulation. The failure was attributed to multiple factors: imprecise electrode targeting, continuous rather than responsive stimulation, heterogeneity in the depression circuits of different patients, and the fundamental problem that depression doesn’t appear to have a single anatomical locus that’s the same in everyone. What worked in Mayberg’s initial patients didn’t generalize to the broader population with the same stimulation parameters.

    The insight that emerged from these failures was that DBS for depression probably can’t be standardized the way DBS for Parkinson’s is. Depression circuits vary between individuals. The biomarker that indicates when stimulation is needed varies between individuals. The brain target where stimulation is most effective varies between individuals. A treatment that works has to be personalized at every level.

    The UCSF closed-loop breakthrough

    This is where the UCSF trial becomes significant. In October 2021, Katherine Scangos, Edward Chang, and Andrew Krystal published a case report in Nature Medicine describing a fundamentally different approach to DBS for depression. Their patient, a 36-year-old woman known as Sarah, had childhood-onset severe depression that had been unresponsive to multiple antidepressant combinations and electroconvulsive therapy. Her depression rating score was 36 out of 54 on the standard scale.

    The team first implanted ten temporary electrodes across Sarah’s brain for a 10-day mapping period. They stimulated each brain region individually while Sarah rated her symptoms, identifying which targets relieved which specific depression symptoms. They simultaneously recorded continuous neural activity while Sarah completed symptom ratings, identifying a personalized biomarker: elevated gamma-band activity in her amygdala correlated with her most severe depressive states.

    They then implanted a NeuroPace RNS System—a device originally developed and FDA-approved for epilepsy—with one electrode lead in the amygdala to sense the biomarker and another in the ventral capsule/ventral striatum to deliver stimulation when the biomarker was detected. The system delivered a tiny pulse—one milliamp for six seconds—only when it detected the neural signature of an oncoming depressive state. Closed-loop. Responsive. Personalized.

    The result was rapid and sustained improvement. Sarah described the initial stimulation as “the most intensely joyous sensation.” Over subsequent months, the device continued to manage her depression in real time. She reported that intrusive depressive thoughts still arose but “it’s just… poof… the cycle stops.” Fifteen months after implantation, the improvement had held.

    The UCSF team has since enrolled additional patients in the trial and expanded to bipolar depression. Mount Sinai performed the first DBS implant for depression as part of a separate clinical trial in March 2025. STAT News identified brain implants for mental health as one of the top three BCI trends to watch in 2026. An IEEE Spectrum analysis published in August 2025 described AI-enhanced DBS that could predict depressive relapses before they occur and adjust stimulation parameters proactively.

    What this doesn’t mean yet

    The honest assessment requires a few buckets of cold water. Sarah is a single patient. An n-of-1 case report, however dramatic, does not constitute evidence that closed-loop DBS will work for depression broadly. The UCSF team has said as much explicitly: “We need to look at how these circuits vary across patients and repeat this work multiple times.” The treatment requires brain surgery—two separate procedures in Sarah’s case. The NeuroPace device is FDA-approved for epilepsy, not depression; its use in the UCSF trial was under an investigational device exemption. FDA approval for DBS as a depression treatment is, in the researchers’ own estimation, still far down the road.

    The earlier DBS trials failed not because the concept was wrong but because the implementation wasn’t personalized enough. Whether the closed-loop, biomarker-driven approach solves that problem at scale—across the enormous heterogeneity of depression as a diagnosis—is an empirical question that will take years and many more patients to answer.

    More than 20 million American adults live with depression, a 60 percent increase over the past decade. Approximately one-third don’t respond adequately to antidepressant medications. For most of those patients, the relevant intervention in 2026 is not an implanted electrode—it’s better access to existing treatments, including TMS and potentially the new at-home tDCS devices. The $500 Flow headset and the surgically implanted closed-loop DBS system represent opposite ends of a continuum, and the clinical reality for most patients with treatment-resistant depression sits somewhere in the middle, where the options are expanding but the solutions are still imperfect.

    The trajectory, though, is unmistakable. The field is moving from treating depression as a chemical imbalance—the serotonin model that dominated psychiatry for decades and has been increasingly questioned—toward treating it as a circuit disorder, where specific patterns of electrical activity in identifiable brain networks produce specific symptom clusters, and those patterns can be detected, modulated, and corrected. That reframing, more than any individual device, is the development worth watching.

    We cover neural stimulation for depression alongside the full landscape of brain-computer interfaces—from motor prosthetics to speech restoration to sensory augmentation—across 48 lectures in our Neuroprosthetics course. If the shift from treating depression as chemistry to treating it as circuitry changes how you think about mental health, the course goes deep on the neuroscience and engineering behind every approach on the spectrum.