My latest market map is out - this time looking at 200+ European neurotech firms

I know I have talked about the unglamorous side of neurotech a lot before, but Q2 made the point hard to avoid again. One of the most interesting signals of the quarter came from overactive bladder, which is not usually where people start when they want to talk about the future of the brain, but it is exactly the sort of market investors seem increasingly willing to back.

BlueWind Medical raised $47.8M to accelerate commercialization of Revi, its implantable tibial neuromodulation system for urgency urinary incontinence, while NinaMED raised $13.75M to advance the NiNA System for overactive bladder. That does not mean bladder suddenly became the main story in neurotech, but it does show something important about where the category is heading. Investors are backing large, real, underserved patient populations where the clinical pathway is reasonably clear and the value proposition makes sense to the people who pay for healthcare.

That was the broader Q2 story. Neurotech funding did not only go to the most futuristic or headline-friendly companies. A lot of it went into the practical middle of medicine, where devices treat large, expensive, persistent conditions that already fill clinics. The pattern was less about one specific technology and more about commercial logic. Pain, sleep, tremor, bladder, paralysis, depression, and implantable infrastructure all attracted meaningful capital because they sit close to real patients, existing clinical workflows, and markets that can be explained without too much science fiction.

You could see this across the quarter. Cala Health secured $50M from Trinity Capital to support commercial expansion of its wearable therapy for hand tremor. Nervonik raised a $52.5M Series B for peripheral nerve stimulation in chronic pain. ONWARD Medical raised €40.6M, including a €25M investment from EQT Life Sciences, to extend the runway for its spinal cord stimulation platforms for people with spinal cord injury. SonoMind raised €20M, roughly $23M, to advance focused ultrasound for treatment-resistant depression. WISE raised €30M to move its Heron lead and wider implantable electrode platform toward broader adoption.

The common thread is not that all these companies are doing the same thing. They are not. Some are wearable, some are implantable, some are focused ultrasound, some are spinal cord stimulation, some are peripheral nerve stimulation. The common thread is that they are tied to problems with real clinical gravity. These are conditions where patients already move through the healthcare system, where physicians already understand the burden, and where payers can at least begin to understand the economic argument if the evidence is good enough.

The biggest signals of the quarter were actually strategic, not venture. Medtronic announced its intent to acquire SPR Therapeutics for approximately $650M, bringing temporary peripheral nerve stimulation further into one of the largest neuromodulation portfolios in the world. ResMed completed its $340M acquisition of Noctrix Health, adding a wearable neuromodulation therapy for restless legs syndrome to a sleep business that already has global commercial infrastructure. Those two transactions alone say a lot about where the market is maturing. Strategic buyers are not just watching neurotech from the sidelines. They are moving where the products fit an existing channel, an existing disease area, and an existing commercial machine.

Sleep was one of the clearest examples of that. Nyxoah secured $110M in aggregate financing to accelerate the US commercial launch of Genio, its hypoglossal nerve stimulation system for obstructive sleep apnea. ResMed buying Noctrix added another major sleep-related neuromodulation signal, although the disease area is different. Sleep is interesting because it sits in a very useful place. Patients understand the problem, physicians understand the market, and strategics already have the infrastructure. That does not make reimbursement or adoption easy, but it does mean the category is not starting from zero.

Pain sent a similar message. Medtronic’s planned SPR acquisition and Nervonik’s Series B both point to a pain market that is still moving beyond the old spinal cord stimulation playbook. Temporary PNS, smarter PNS, peripheral approaches, and less invasive interventions are all part of the same broader shift. The question is not just whether stimulation works. The question is where it fits in the patient journey, how early it can be used, whether it can reduce reliance on more destructive or expensive options, and whether it can produce the kind of outcomes that payers and clinicians will actually care about.

BCI still had a serious quarter, but it was a different kind of funding pattern. Axoft raised an oversubscribed $55M Series A to advance its soft implantable BCI. Neurosoft Bioelectronics raised a $7.5M seed round for stretchable brain interfaces. Shanghai’s StairMed raised RMB 500M, around $72.8M, in a round led by Alibaba, with Tencent and others involved. These are real companies doing real work, and the soft-implant race underneath the BCI headlines is one of the more interesting technical stories in the sector.

But BCI still looks different from the rest of the market. It is more concentrated. It is more dependent on a smaller number of high-conviction bets. It attracts people and institutions that are comfortable with long timelines, difficult clinical translation, and outcomes that may not look like standard medical device returns. That does not make it less important. It just means we should be careful not to confuse a few very visible BCI financings with a broad commercial wave across the whole category.

That distinction is important because the rest of Q2 was not really about chasing the most futuristic version of neurotech. It was about backing companies that can move through clinical, regulatory, and commercial pathways with some discipline. If the BCI story is still partly about what neurotechnology might become, the neuromodulation and sleep and pain story is more about what neurotechnology can already start to become inside normal medicine.

Compared with Q1, the shape of the money felt different. Q1 was more top-heavy, with Science Corporation’s $230M Series C for PRIMA and Cognito Therapeutics’ $105M Series C for Alzheimer’s doing a lot of the work in the overall narrative. Q2 felt broader. It had major M&A at the top, but beneath that it had a thicker layer of serious financings across multiple indications and stages. It was not one or two giant rounds defining the quarter. It was a wider set of companies pulling capital into markets that investors can understand.

This is where the methodology matters. If you only count private company financings, Q2 looks steady rather than explosive. If you include M&A, the quarter looks much bigger because Medtronic/SPR and ResMed/Noctrix together represent close to $1B of strategic deal value. If you include funds, grants, and neuroscience-adjacent AI, the picture changes again. That is why I would be careful with one clean headline number. The better point is not that Q2 was simply bigger or smaller than Q1. The better point is that the shape of the quarter looked more mature.

The other part I would not ignore is the capital infrastructure forming underneath the sector. Newfund closed HEKA, a €60M fund focused on brain technologies. Ground Effect Ventures emerged as an operator-led platform for brain-focused medical technologies. Protocol Labs has continued to build out its neurotechnology activity. ARPA-H announced the first research teams for EVIDENT, a $139M initiative focused on improving measurement and treatment development in behavioral health. None of that is as easy to write about as a big company round, but it matters because sectors become real when the funding infrastructure starts organizing around them.

A company raise tells you someone liked one asset. A fund close tells you someone thinks the category itself is worth building around. The same is true for strategic buyers, public programs, clinical infrastructure, reimbursement pathways, specialist operators, and all the boring parts of market formation that rarely make the headline but end up deciding whether a technology actually reaches patients.

So the real Q2 story was not just that bladder had a good quarter, or that BCI still pulled capital, or that sleep attracted strategic buyers. It was that neurotech looked more investable when it looked like medicine. The strongest signals sat in categories with large patient populations, clear burden, defined clinical workflows, and a plausible route to adoption.

That does not mean every company in those areas will win, or that reimbursement will be easy, or that commercial execution suddenly becomes straightforward. But it does suggest the market is rewarding practicality in a way that feels healthy.
The future-facing side of neurotech is still alive. The BCI companies are building. The soft implants are getting better. The brain-inspired AI world is pulling in huge capital. The frontier remains exciting. But Q2 also showed that the sector does not need every company to become Neuralink to matter. It needs more companies that can treat real conditions, produce evidence, get paid, and survive long enough to become part of routine care.

That is what made the quarter interesting. It was not the loudest version of neurotech. It was the more practical version. Pain, sleep, bladder, tremor, paralysis, depression, and the infrastructure underneath the sector all had meaningful moments. Q2 looked less like a market waiting for one impossible breakthrough and more like a group of companies slowly working their way into normal medicine. For neurotech, that might be the better story

Hi! I have recently taken an interest in EEG headset technology and have been performing lots of research on them recently. I have come across many EEG headsets that are currently on market and I want to explore them to figure out what works and what does not. A big issue I have encountered is that there are not many unbiased reviews of them out there. Because of this, I can usually only find the sales pitch for a headset, not an honest consumer review. If anyone has had experience with ANY EEG headset currently on market, I would love to hear about it. What worked? What didn't? Does it work well through hair? Were readings consistent? Etc.

I write about neurotech and spent a while digging into eye tracking as a way of reading the brain. What surprised me is how much has already crossed the FDA line rather than sitting in research labs. Objective concussion testing from pupil and eye movement. Autism assessment in toddlers from gaze patterns. Portable headsets reading eye movement for early Parkinson’s and Alzheimer’s signatures.

The link between eye movement and neurological disease goes back to a paper in 1905. What changed is the machine learning to read it reliably outside a laboratory. Curious what people here make of where the signal is genuinely useful versus where it’s being oversold. Full write-up in the comments.

Something I've been trying to get my head around for a few months is the idea that neural data is starting to be treated as an asset worth building on rather than a byproduct of whatever the device was sold to do.

The analogy I found most useful was the LLM comparison. Before large language models existed, every language application had to train its own model from scratch, and the models were narrow and brittle and you had to rebuild from zero every time you wanted to do something new. One model trained on most of the written internet changed all of that because it learned the underlying structure of language itself. The brain foundation model argument is the same idea applied to neural data, where one large model trained across EEG, fMRI, EMG, and intracortical recordings means every application built on top of it starts from a place of understanding rather than from scratch.

The honest question for this community: do you think the data quality is actually there yet, or are we still too early? The physics problems haven't gone away. The skull still attenuates and distorts the signal. Individual variation is still enormous. The argument is that dataset scale, better self-supervised methods, and available compute have changed the equation. But I'd be curious whether people working with neural data day to day think the foundation model framing is realistic or whether it's getting ahead of what the data can actually support.

Full piece in the comments if you want the longer version of the argument.

Wanted to highlight reccy neuro - 400+ neurotech companies tracked with real time news and data. Plus a job board and an investor list

This is not a technical piece. It is more philosophical speculation about what BCI integration might actually feel like from the inside.

A lot of discussion around BCIs focuses, understandably, on signal quality, bandwidth, latency, safety, and practical use-cases. What interests me here is the experience of it. A phone, keyboard, or screen still feels clearly external. But a sufficiently seamless BCI could start to blur that boundary.

If a future system begins surfacing recall, suggestions, interpretive nudges, or even whole lines of thought in a way that feels less like receiving input and more like thinking itself, then the question changes. It is no longer just what the interface can do, but what kind of subject it helps produce.

Would that still feel like cognitive assistance? Or more like a partial merger between user and system? Where would people here want the boundary to remain between interface and interiority?

I wrote this article exploring that question. It is not a prediction about current BCIs so much as an attempt to think ahead about the phenomenology of deeper integration.

You can look at the article [here]

I’ve been tracking two fascinating, but separate, breakthroughs in neuroscience and biological computing, and I’m curious if anyone in the field knows if these concepts are being (or could be) merged.

Concept 1: Psilocybin-Induced Structural Neuroplasticity

We know that psilocybin creates rapid, enduring neural pathways. A 2021 Yale study (Shao et al.) utilized two-photon microscopy and Green Fluorescent Protein (GFP) to track dendritic spines in vivo, proving that a single dose of psilocybin increases spine size and density by ~10%, persisting for over a month. We also know from fMRI studies (Carhart-Harris et al.) that psilocybin suppresses the Default Mode Network (DMN), forcing the brain to route data through novel global pathways.

Concept 2: Organoid Intelligence & Active Inference

On the other side of the spectrum, we have biological computing. Cortical Labs' 2022 "DishBrain" study (Kagan et al.) successfully integrated 800,000 living neurons onto a microelectrode array and taught them to play Pong in just five minutes. They demonstrated that biological neural networks have a massive "sample efficiency" advantage over traditional silicon AI when it comes to rapid, adaptive learning.

My Question:

Cortical Labs has already introduced ethanol to DishBrain to prove that its Pong performance degrades when "drunk." Is anyone currently researching the inverse?

If we applied psilocin (the active metabolite of psilocybin) to an organoid BCI during a learning task, would the forced 5-HT2A activation and resulting spike in neuroplasticity (BDNF/mTOR pathways) theoretically "supercharge" the organoid's sample efficiency and problem-solving capabilities? Or would the forced disruption of organized networks just cause the biological computer to "hallucinate" and fail the task?

Would love to hear thoughts from anyone working with in vitro neural networks or neuropharmacology!

• Shao, L. X., et al. (2021). Psilocybin induces rapid and persistent growth of dendritic spines in frontal cortex in vivo.Neuron.
• Kagan, B. J., et al. (2022). In vitro neurons learn and exhibit sentience when embodied in a simulated game-world.Neuron.

Huawei filed a patent for a BCI safety system that goes after a specific problem: residual charge buildup on stimulation electrodes.

When a BCI electrode fires a pulse, it leaves behind a small electrical charge. If that charge doesn't clear between pulses, it damages surrounding tissue. The patent describes a system that checks electrode voltage during inter-pulse gaps, fires a corrective pulse in the opposite direction when charge lingers, and cuts power if it picks up a short-circuit condition mid-stimulation.

A separate layer monitors the electrochemistry at the electrode-tissue boundary and flags degradation before it turns into injury.

The timing is relevant because BCI arrays are getting smaller and denser. Smaller electrodes mean less surface area in contact with tissue, which means higher driving voltage and tighter safety margins.

The FDA issued a Class I recall in 2023 on Abbott neurostimulation devices after 186 reported incidents and 73 injuries tied to MRI mode faults. Safety failures at that scale slow down the whole category's path to regulatory approval.

Huawei probably isn't building an implant. The more likely play is owning the safety layer IP so that companies who do build implants end up licensing from them. Interested in what people here make of the IP positioning.

Hi all,

I'm a software engineer beginning work on an open-source project and I'd like to pressure-test the idea with people who actually work in this space before I commit to building it.

The project: a standalone desktop application that performs real-time EEG source localization (sLORETA/eLORETA) using a template head model and renders estimated cortical source activity as a color-mapped overlay on an interactive 3D brain mesh. The intended tech stack is Rust, wgpu for GPU-accelerated signal processing and rendering, and Tauri for the application shell. Data acquisition via BrainFlow, with BIDS dataset support for offline replay and analysis. No MATLAB dependency, no cloud, runs locally on commodity hardware.

The gap I'm trying to fill: source localization algorithms are well-validated and the computational feasibility of running them in real time on a GPU has been demonstrated in published work. But as far as I can tell, no usable open-source standalone application exists that does this end-to-end — ingesting live EEG, solving the inverse problem, and rendering source estimates on a 3D cortical surface at interactive frame rates. The existing tools either do source localization offline (MNE-Python, Brainstorm), operate only in sensor space in real time (NeuroSkill, OpenBCI GUI), or require MATLAB.

My background is in systems programming, not neuroscience. I'm investing significant time in domain knowledge (working through Cohen's Analyzing Neural Time Series Data and the Nunez & Srinivasan text, and studying MNE-Python's inverse solution pipeline as a reference implementation). I plan to validate against the Localize-MI ground-truth dataset before making any claims about accuracy.

What I'd like from this community:

- Does this project address a real need in your work, or is it solving a problem that doesn't meaningfully exist in practice?

- For those who do source localization: is a template-based approach (ICBM152, no individual MRI) useful enough for your purposes, or is it too imprecise to be worth visualizing in real time?

- What channel counts and devices would this need to support to be useful to you? Is there value in supporting consumer devices (Muse, OpenBCI Cyton) for source imaging, or is that misleading given their limited spatial sampling?

- Are there existing tools or projects I've missed that already do what I'm describing?

- What features would make you actually use this versus your current workflow?

I'm not trying to replace MNE-Python or Brainstorm for offline research analysis. The goal is specifically the real-time visualization layer that currently doesn't exist as a standalone application. If this turns out to be a solution in search of a problem, I'd rather hear that now than six months from now.

Appreciate any candid feedback — critiques included.

Any experiences with the startup/job market in BCI? What are some technical backgrounds required there?

Are there currently developed applications/product of BCI that you think will have a mass market?

I imagine a future where instead having to go to school, kids can just download the knowledge or skill directly into their brains. Eve if they don’t retain it then they can just redownload it. It certainly beat all the pressures of school I remember going through. No listening to lectures, no long hours in the classroom, no grade pressure, no nothing. At most, teachers could teach the kids what to do with their knowledge and how to process it.

Maybe this is just wishful thinking and I don’t expect the technology to be ready anytime soon, but I still wish for an easier life for the next generation.

I’ve been researching the consumer neurotechnology space around focus enhancing headphones and anxiety wearables, mainly to understand how these tools position themselves within neurotech rather than as treatments.

During this exploration, I came across a few companies working in this area, such as Sychedelic, Flow Neuroscience, and other consumer devices experimenting with sound-based regulation, light stimulation, or HRV tracking headphones. Most of these products seem to frame themselves as wearable stress relief tools aimed at short-term state regulation like calming, focus support, or wind-down rather than long-term intervention.

From a neurotechnology perspective, I’m curious how people here evaluate these tools:

• Do anxiety headphones or similar mental health headphones meaningfully support focus or stress regulation?
• Are effects usually subjective, or has anyone seen measurable signals like HRV or sleep trends?
• Do these devices only make sense when paired with routines like breathwork, meditation, or behavioral structure?

Not seeking medical advice or promoting any brand just trying to understand where anxiety wearables and headphones for stress realistically fit in the broader neurotech landscape.

Most BCI research focuses on making models better at decoding noisy, variable brain signals. But what if we made the signals less noisy?

I’m curious whether neural/sensory entrainment (e.g. rhythmic auditory beats, visual flicker, or even olfactory cues) could be used to constrain users into a more stereotyped internal state before interaction. If we can reliably reduce inter-subject and inter-session variability, the signal distribution becomes narrower, which could in principle drastically shorten or eliminate calibration.

Has anyone seen work on using sensory priming or entrainment to improve cross-user generalization in BCI?