The Non-Sci-Fi Story of Brain-Computer Interfaces: A Conversation with Ximing Wang of "Kongshan Ci" | Yunqi Podcast Attent!on
Beneath the Brain, Above the Signal
Over the past few years, AI has been sprinting forward at a staggering pace. We've used "neural networks" to simulate the brain's wiring, deployed large models to approximate language, reasoning, and memory, and grown accustomed to understanding intelligence as something trainable, deployable, and callable. Yet the brain itself — the organ AI repeatedly invokes as metaphor — remains shrouded in fog.
From the first electroencephalogram trace recorded in 1924 to Neuralink reigniting public imagination around brain-computer interfaces, humanity's exploration of brain signals has never stopped. The most eye-catching aspect has been the cool factor: capturing signals, controlling cursors or robotic arms with thought, reconnecting with the external world.
But that's not the only destination worth reaching. Emotion, sleep, cognition — these invisible burdens of daily life are becoming the real entry point for brain-computer interfaces to reach ordinary people. In this issue of "Attent!on," we want to hear a non-sci-fi story about brain-computer interfaces with you. **
Our guest this issue is Ximing Wang, co-founder of Kongshanci, a brain-computer interface project backed by the Yunqi–Shanghai Jiao Tong University AI Angel Fund.
In 2024, as large models and agents dominated the conversation, Ximing Wang — Cambridge economics background, years spent developing medical data algorithms — chose to team up with people from "heavier" backgrounds: surgical robotics, medical imaging cloud, long-time specialists in neuromodulation, simulation, and transcranial focused ultrasound.
Together they built Kongshanci, stepping into a hardware-heavy, long-cycle track: mental and cognitive disorders.
This is a chronic gap facing structural bottlenecks. Take depression: traditional medication, psychotherapy, and physical therapy all play roles, yet they struggle with long cycles, high individual variability, and delivery that resists scaling.
What Kongshanci wants to build is a more precise "circuit diagram" for the complex activity beneath the skull: first making the invisible signals, pathways, and abnormal changes visible enough, then applying the right physical energy for targeted intervention, bringing disordered systems back toward stability.
This is Ximing Wang's slice of understanding brain-computer interfaces: not merely "reading the brain to control external devices," but translating invisible emotional and cognitive damage into readable, quantifiable, verifiable signals and energy — then intervening at the right location.
Along this path, Kongshanci is building a brain-computer interactive neuromodulation platform for mental and cognitive disorders. In other words, they're not chasing cooler science fiction, but offering a more verifiable, more reachable intervention path for brains worn down by everyday invisible burdens.
This issue's conversation:
Ximing Wang
Co-founder, Kongshanci
Na Li (Linda)
Managing Director, Yunqi Capital
01 Going Deeper Into the Brain
Linda:
Many founders start because something specific happened around them, some dramatic story. What brought the Kongshanci team together to tackle emotional and cognitive problems?
Ximing Wang:
Our team has one major characteristic: everyone genuinely empathizes with depression, cognition, these issues. Some have experienced related struggles themselves; others have watched family or friends sink into them.
Emotional and cognitive problems run through a person's entire life. We're not just focused on depression, but on overall emotional quality. Kongshanci will start from one point, do it well, then continuously expand our boundaries and capabilities.
Linda:
When you first started planning the company, why choose brain-computer interfaces and neuromodulation to solve this?
Ximing Wang:
I've been working in this direction for the past six years. I'm personally very interested, and I see a clear trend.
My background was more purely algorithms and data, but I was very convinced that on the hardware side, in the combination of signals and energy, something better would emerge. So when we started planning the company in early 2024, our first internal discussion was about solving this through brain-computer interfaces.
It's just that at the time, the market hadn't fully bought into this broad direction. So when building the product, we narrowed our scope once, moved forward in small enough units first, then gradually spread our philosophy to the industry and beyond.
Linda:
When did you start thinking about raising your first round? How did you connect with Yunqi?
Ximing Wang:
At the very beginning, we pooled some money ourselves with many friends and just started moving. The company was founded in October 2024. After the demo was done, we got our first angel round.
Later we were fortunate to meet Mr. Mao. After about 30 minutes of conversation, he was quite firm on this direction. Then Yunqi colleagues dug deeper, and we gradually showed the full matrix vision — including how brain-computer interfaces would be deployed in emotion and cognition, how to integrate with AI — and were further recognized by Yunqi, leading to the Pre-A round. In the recent Series A, Yunqi also continued to double down.
Linda:
What was your impression of Mr. Mao in those 30 minutes?
Ximing Wang:
Warm and refined. Mr. Mao was probably the first investor we'd met with a heavier technology label. His dimensions and perspective for thinking were different from traditional medical device investors. So the communication flowed quite smoothly. I'm not from a particularly traditional medical device background either, so I could better express what we actually wanted to do. Things like globalization, moving toward the consumer end in the future — these aren't particularly conventional paths in traditional medical devices. But emotion and cognition have strong everyday attributes; they happen at home, in the small moments of life.
02 Demand and the Gap
Linda:
From a medical data perspective, where do issues like emotion, cognition, and sleep sit within the healthcare system?
Ximing Wang:
Many people initially think of them as some specific category of disease, but actually, within the entire healthcare system, they're more like "capillaries" — genuinely present in any scenario.
I once had a fairly serious sports injury, torn knee ligaments with fractures and bone cracks. During surgery and recovery, the psychological trauma was enormous. You keep replaying the injury, with lots of worry, frustration, and anxiety.
So these issues are everywhere in healthcare and need more recognition and acknowledgment. The insufficient attention in the past was partly a cognition problem, and partly because solutions weren't systematic enough or effective enough.
Linda:
For depression specifically, where do the real treatment bottlenecks lie?
Ximing Wang:
About 30% of depression patients have treatment-resistant depression and don't respond well to medication. Another figure: roughly 40% of depression patients in China need to visit three or more hospitals before getting meaningful relief. This really reflects the industry's past situation: because treatment methods weren't efficient enough, many people, after encountering problems, were somewhat left knocking on doors everywhere.
We entered this industry because we observed this. We spent a year embedded in hospitals, observing patients in different clinics, conducting extensive interviews with patients and doctors. Whether they felt their diagnosis was the problem or treatment was the problem; whether treatment took too many days or side effects were too severe. These were foundational homework for us to build systematic understanding.
Linda:
Where do medication, psychological counseling, and physical therapy each hit their ceiling?
Ximing Wang:
Traditional medication has two fairly consensus problems. First, the effective rate isn't that high, roughly 30% to 40%. Second, side effects persist — weight gain, appetite-related issues, or other side effects. The medication cycle is also long, possibly months or even a year.
Psychotherapy has developed rapidly in recent years, but its core problem is delivery consistency. It heavily depends on experience, training, and long-term stable relationships.
Physical therapy itself is an effective approach, but it also had major problems in the past. For example, some treatments require 6 to 10 weeks, going back and forth to the hospital. Going to the hospital every day already discourages many people. And after 6 to 10 weeks, the effective rate is also roughly 30% to 40%. This means patients invest a lot of time and energy, may not get cured in the end, and might even have symptoms worsen due to disappointment.
Linda:
Beyond depression, do cognitive disorders, sleep disorders, and anxiety face the same supply-side predicament?
Ximing Wang:
These directions share two commonalities. First, if we call this category demand-side, then its supply-side is severely insufficient. Good solutions that can meet this demand are extremely scarce. Second, they're all related to the deep brain. The deeper you go, the higher the research difficulty, the greater the complexity, and the finer the functional partitioning.
This is also one reason we believe this domain has enough runway for continuous refinement over a long time.
03 Another Imagination
Linda:
When people mention brain-computer interfaces, the first reaction might be Neuralink, craniotomy, electrodes. What is Kongshanci's understanding of brain-computer interfaces? Do you also need to open the skull?
Ximing Wang:
That depends on what stage we're at and what problem we're solving. One very important point for us: we're a product company. We'll follow technology development, have colleagues with different backgrounds join, and make excellent products.
We're not a completely non-invasive brain-computer interface company. We're also continuously investing in and observing invasive directions; it's just that at this stage, products haven't formed yet, so we won't disclose them externally. Our style is basically to only speak after it's mostly done.
We don't think brain-computer interfaces should be strictly divided by invasive versus non-invasive. What matters more is: what problem does it actually solve? Is it solving emotion and cognition-related problems, or motor intention problems? The purpose determines the technical path in front, and also determines the relatively optimal solution at the current stage.
Linda:
When is invasive needed, and when is non-invasive suitable?
Ximing Wang:
Take playing piano as an example — fingers moving at high speed continuously, this relates more to motor intention, with very high temporal resolution requirements. But if you switch to emotion, a person switching between ecstasy, grief, calm, and rage isn't changing at high speed like finger movements. Emotion is relatively more stable; you don't need to frantically decode every second's change. So its solution is different from the skull-opening approach for reading motor intention.
Linda:
If you were to introduce it in relatively accessible language, what problem is Kongshanci actually solving?
Ximing Wang:
You can look at it in stages. In stage one, we hope people recognize: we're a company using brain-computer interface methods to treat depression, providing solutions with high efficiency and strong recognition from doctors and patients.
In stage two, we hope people recognize our capabilities across the full energy spectrum. We call it a brain-computer interactive neuromodulation platform. We'll combine different signals with different energies, focus on the mental and cognitive disorder domain, and make excellent products and solutions.
Linda:
You just mentioned the bottlenecks of medication and psychological counseling. But the public might have a more fundamental question: we can more easily understand taking medicine to regulate chemicals. But magnetic, electrical, ultrasonic — these physical energies, why would tapping on the brain improve mental illness or sleep problems? What actually happens to the brain under physical energy stimulation?
Ximing Wang:
The development of this discipline is somewhat the reverse of medication. Many neuromodulation technologies originated from observations, or accidental phenomena, with scientific investigation following.
To date, human understanding of the brain is still in a very early stage. The mechanisms behind many phenomena are complex and not fully understood. But over the past decade or two, understanding of how these physical energies affect the brain has continuously been refreshed.
**Take magnetic stimulation as an example. The most basic principle can be understood this way: **a large current instantly passes through a coil, generating a pulsed magnetic field. After this magnetic field passes through the skull and brain tissue, it induces an electric field locally. What actually drives changes in the neural membrane is this induced electric field. Researchers then observe neuronal firing, neurotransmitters, EEG rhythms, and deep brain region activation — it involves both electrophysiology and chemophysiology, ultimately possibly acting together to improve symptoms.
Some of the latest research continues pushing this understanding forward. For example, some studies have found that pulsed magnetic stimulation may enhance synaptic plasticity in neurons.
04 From Papers to Clinical Answers
Linda:
Research in this field is progressing quickly, so why hasn't clinical translation been ideal?
Ximing Wang:
The reason it's not ideal, we believe, is that this is a systems engineering problem. Take depression as an example: it involves diagnostic precision, degree of personalized treatment, device performance itself, execution precision and efficiency, and prognosis management. If any link has obvious shortcomings, the final treatment outcome will suffer greatly.**
Most pure technology perspectives may focus more on major breakthroughs at a single point, like improving efficiency at one technical point, but overlook the systems engineering problem.
So in our team, clinical is also a very important R&D department, not just doing clinical trials. It's part of comprehensive capability. Many teams domestically and internationally still approach this in a point-based way, trying to solve problems. From the start, we hoped to spread more evenly. For example, early in the company's founding, we invested in simulation capabilities because it provides great help for overall system performance.
Linda:
Your first clinical patient gave the team a lollipop on day five of treatment. Can you tell that story?
Ximing Wang:
This has always been a case we're very proud of. The team spent a long time on R&D. Although we had confidence in the product itself, before actually treating the first patient, everyone was actually nervous. The first non-patient was myself — I completed the full treatment, so I knew very well what it felt like.
The first depression patient, the first two days showed no visible change at all. When she arrived, she still looked quite severe, unwilling to interact with others. After treatment ended, she'd hide in a corner, not speaking. When the hospital offered water, she'd very politely decline — overall a fairly closed-off state.
But by day four, she suddenly washed her hair, combed it, and put on lipstick to come for treatment. The entire treatment room was shocked. Because past therapies typically required 6 to 8 weeks, but she showed obvious change on day four.
We followed up and found she'd also cleaned her home, which she hadn't touched in half a year, and had specially dressed up and changed clothes to come for treatment. This case was hugely motivating for the team. Whether colleagues working on electronics or algorithms, everyone was very energized.
05 The Product Power of Brain-Computer Interfaces
Linda:
After the device entered hospitals, how did doctors view your product?
Ximing Wang:
Doctors felt it was very satisfying to use.
This was actually what we spent the most time discussing when defining the product. In traditional industries, others also make products, but often you see thinking about one thing but not two, thinking about two but not three.
What we hope to achieve: first, patients feel the efficiency is sufficient; second, doctors also find it easy to use. If patient outcomes are good, but each use requires several people spending several hours, then scalability and recognition face major challenges.
So we try to optimize the product as much as possible.
Linda:
Hospitals should care a lot about safety. How do you convince them?
Ximing Wang:
The bottom line of medical devices is safety. Many colleagues on our team come from the surgical robotics industry and other medical device industries, with sufficiently high standards to begin with. We do extensive rigorous testing and verification to ensure product safety.
Only the second step is clinically validating effectiveness. Safety is an indispensable condition in the medical device environment.
The same applies to sleep devices. Many people overlook the safety of sleep intervention itself. For example, intervening at the wrong time could actually cause disruption. Some teams might not recognize this problem and act somewhat aggressively. We tend more toward ensuring sufficient safety first, then discussing how to achieve extreme effectiveness.
Linda:
You just mentioned the sleep product. What kind of product is it?
Ximing Wang:
The sleep product is something our entire team really likes. Internal testing is done, and results are quite good.
Our approach is completely different from what's on the market. It's a sleep-focused product that analyzes EEG, then uses electrical intervention to improve overall sleep quality. The entire experience is very imperceptible — wearing it all night is like wearing an eye mask.
Many people, hearing electrical stimulation, might wonder if it stings, but it's actually not like that.
Linda:
How is this different from sleep monitoring technology in hospitals now, or some consumer-grade sleep products on the market?
Ximing Wang:
It's actually completely different. What we do relates to the deep brain, and has a lot to do with functional partitioning of the deep brain. Each brain region's mechanism is different; although they may seem connected, internal division of labor is actually very clear.
Take sleep as an example. We combine real-time EEG feedback with dynamic parameter adjustment in electrical stimulation regulation. You can understand it as not just choosing when to stimulate, but also intensity, frequency, and parameters — there's a lot of customization inside. So it's actually quite "medical," a different concept from common solutions on the consumer market.
What we do is targeting specific deep brain nuclei and specific waves during particular sleep stages, using induction to improve sleep. Many products on the market are more about relaxation, soothing, or showing users some EEG fluctuation charts — they're not solving problems at the same level as what we're addressing.
06 From Electrical Stimulation to Ultrasound, Going Deeper
Linda:
You have both hospital-facing medical-grade products and home/personal-facing consumer-grade products. How consistent is the underlying technology between the two?
Ximing Wang:
We currently have three products: one already capable of clinical deployment and solution delivery in hospitals; one is the next-generation ultrasound brain-computer interface technology we believe in; and one is a home-facing product combining EEG and electrical stimulation.
They share several commonalities.
First, understanding the brain itself is an important prerequisite for the entire development. You need to know exactly what to treat and where to target before doing subsequent steps. Otherwise it's like building a big hammer and banging everywhere without knowing what to solve.
Our development logic is the reverse: first being very convinced a certain direction can work out, then systematically making it into a product.
Second, hardware, software, algorithms, and system delivery capabilities are interconnected. For example, how to design better electronics, how to design better structures, how to do doctor-device interaction, how to do sub-skull simulation algorithms. Light, sound, electricity, and magnetism have different paths, but share commonalities in simulation and system design.
So experience from the first product greatly helps the second and third. Because from the start, we viewed every technical path and every product with systems thinking.
Linda:
You're also advancing ultrasound brain-computer interfaces. Most people's understanding of ultrasound comes from B-ultrasound in hospitals. How can this become a tool for treating brain nerves and cognitive disorders?
Ximing Wang:
Ultrasound is quite interesting as an energy. It can be used for imaging, for regulation, and even simultaneously for imaging and regulation. This is also an important characteristic of it as a brain-computer interface technical path.
In application, ultrasound broadly involves mechanical effects and thermal effects. Thermal effects can be understood as energy accumulation over a certain time, while what we mainly utilize now are its mechanical effects, for imaging and regulation.
Ultrasound waves cause cells to undergo periodic compression and stretching, driving cell membrane deformation, possibly also pulling on the cytoskeleton, enhancing cytoplasmic flow. More importantly, it may open mechanically sensitive ion channels on the cell membrane. That is, when ultrasound exerts mechanical force on cells, such ion channels open, further forming some of the mechanistic pathways in neuromodulation.
Ultrasound can also combine with many things — microbubbles, modified proteins, or some particles. This involves cavitation effects. For example, inertial cavitation instantly generates local pressure changes, which is a key effect in blood-brain barrier opening and drug delivery.
If regulation solves "how to influence the brain," then imaging solves "how to see the brain." As an imaging tool, ultrasound shares commonalities with the familiar B-ultrasound. B-ultrasound works well for viewing soft tissue, with high repeatability and relatively simple operation. But applied to the brain, the biggest difficulty is the skull.
Linda:
Why does ultrasound lose focus when penetrating the skull? Was this the original technical difficulty?
Ximing Wang:
Yes. The skull is inherently a protective structure; it doesn't easily let various external energies into the body. When ultrasound waves pass through the skull, relatively unpredictable deflection, defocusing, and distortion occur. Actually, it's not just ultrasound — light, sound, electricity, and magnetism all encounter some degree of unpredictability when passing through the skull. For example, light has diffraction, scattering, and deflection; other energy forms encounter similar problems.
But as technology advances — for example, CT imaging getting better — we can first use CT to very finely reconstruct skull structure, then do physics-based simulation based on this structure. This makes it possible for ultrasound energy from different directions to still precisely focus on the same point after passing through the skull.
This involves many technology layers: transducer design, drive circuits, matching circuit control, medical imaging understanding, physics simulation, and tuning between simulation results and the real world. Why we can go very deep and achieve very small focus is essentially the result of multiple technologies working together.
Our current path is to start with focusing, then gradually build up imaging capabilities. Ultimately, we hope to achieve real-time imaging and regulation, forming a closed-loop ultrasound brain-computer interface architecture.
Linda:
Medical and consumer are completely different R&D and commercialization rhythms. How do you balance them? Will the consumer side feed back to the medical side?
Ximing Wang:
We believe consumer will definitely feed back to medical. Consumer-side reputation and recognition will feed back to medical-side recognition. But this also places very high demands on team organization.
Linda:
Thank you Ximing for such a systematic explanation and sharing today. One thing that struck me deeply after this conversation is that Ximing wasn't talking about product features, but about a direction he truly believes will happen.
I can also feel that Kongshanci is a team with strong empathy. They truly understand the struggles people are experiencing, and are willing to use technology to respond to these real needs. Such a team is vivid, and has conviction and pursuit. On behalf of Yunqi, I sincerely wish Ximing and the Kongshanci team continued progress in pushing forward this long-term and important endeavor.
Finally, to reintroduce: Kongshanci is a portfolio company of the Yunqi–Shanghai Jiao Tong University AI Fund. This is an early-stage frontier technology fund we launched with Shanghai Jiao Tong University in 2025, focusing on artificial intelligence, embodied intelligence, brain-computer interfaces, nuclear fusion, AI for Science, and other emerging and future industries.
We pay attention to these fields not because they're already ready for explosive growth today, but because we believe that technologies that truly change human lifestyles often begin quietly accumulating and growing long before they become mainstream.
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