r/BCI

Hello everyone i am planning internationally soon from Germany to India with a layover at the Doha airport. I have an Open BCI ultracortex Mark IV, I wanted to know if you faced any issues or difficulties transporting the EEG headset through an airport, maybe at the security checks or anywhere else.

The headset belongs to me, a bachelor's student, I want to carry it with me so that i can work on projects during my summer vaccation.

I run a neurotech recruitment, market data and media business and finally did something I had been putting off, placing every funded company by headquarters and then doing the same for the investors. 564 companies, 107 investors I could confirm. 330 of the companies are American, and all of Europe combined is 165. The investor side is even more concentrated, 81 of the 107 are US-based. What struck me is that US investors clearly fund a lot of the non-US companies too, so the geographic gap in where the money comes from is even wider than where the companies are. Happy to talk through the method or the gaps in the comments. Full write-up with the charts and the investor list is linked below.

Hi everyone, I am an undergraduate majoring in Biology. I took some classes in Neuroscience, math (Multivariable calculus and Linear Algebra) with intro classes in CS and Physics.

I just discovered BCI technology for Speech neuroprosthesis and really want to do research in this field. I am currently an intern in a noninvasive bci lab and my role is to processing EEG signal

I feel that my background is not enough and want to learn more to enter this field. Does anyone have any recommendation on where to start or what should I focus because I am quite lost now. I want to focus more on the neuroscience and linguistic aspect of this rather than the coding/ ML aspect.

Thank you so much

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

Think to build my own homemade HPC for computer modeling of proteins such as receptors, membrane proteins, Huntingtin. Want to compute interactions of proteins with neurons.

What you think about hardware?

P.s. absolute zero in AI, do i need hardware for it?

I got the 14 channel Emotiv EPOC X and I want to get rid of it. It still is in the box packaging I received it. Open to offers. Images on request

I track neurotech transactions for a market map I maintain, and I sorted the whole thing by the disease being treated rather than the technology. A few things stood out for this sub specifically.

Paralysis and communication is the single biggest money-in category, north of a billion dollars. Neuralink, Synchron, Precision, Paradromics, ABILITY, Axoft and the weird new kids on the block Subsense building a brain interface delivered through nanoparticles up the nose. Six companies, six different hardware bets and probably a long way from any acquisitions

The pattern across the whole map: the indications raising the most are the ones nobody has ever acquired. The actual exits are all in the unglamorous stuff, bladder, spine, airway. Proven gets bought, promise gets funded, and they almost never overlap.

Full artcile in the comments

I've tracked sleep, HRV, glucose, and activity for years. The thing I've never been able to measure well: what's actually happening in my brain during the day. Am I in flow or just awake and when do I best engage into a deep level of engagement in cognitively intense content vs. creativity? And when do I approach fatigue or fine for another hour?

I'm exploring building something that fills this gap like something you wear that tells you your mental state in real time and is private-first. Something that's not like a mood tracker (which requires you to notice and report) but passively, the way Oura tracks your sleep without you doing anything.

Would love to hear from people who've gone deep on QS: is this a felt gap? Have you found anything that does this already? And what would you need to see in terms of data quality, privacy, form factor to actually trust and use something like this?

Specifically curious about:

• Do you find your body metrics (HRV, readiness) actually predict your cognitive performance? Or is there a gap?
• Would real-time mental state awareness change how you structure your day?
• What would make you trust or not trust a device like this?
• Are there any wearables that are doing a good job at addressing the attention problem already?

This community's input would genuinely shape what's the best way to build something useful.  Genuinely trying to understand whether this is worth building. Brutal honesty welcome! Thank you!!

Hi everyone,

We just open-sourced NeuraDock Visual Cognitive Load Agent, a local-first EEG agent that turns EEG data into a real-time cognitive-load API for AI agents, BCI prototypes, HCI/XR systems, and adaptive interfaces.

The idea is simple:

Instead of treating EEG as just offline signal analysis, we want to make it usable as a local API:

EEG file / synthetic replay / NeuraDock hardware
        ↓
preprocessing + signal quality gating
        ↓
alpha dynamics + visual cognitive-load estimation
        ↓
local dashboard + API endpoint
        ↓
AI agents / XR / HCI / neurofeedback apps

A few things we wanted to make clear:

• No hardware required to start: you can run the agent with synthetic replay or example EEG data.

​

git clone https://github.com/Neuradock/eeg-workstation-agent.git
cd eeg-workstation-agent
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
neuradock-agent serve --port 8765
# open http://127.0.0.1:8765

• Works with hardware too: if you have NeuraDock EEG hardware, it can run as a real-time closed-loop cognitive-load monitor.
• Full toolchain is open-source: data loading, preprocessing, quality control, cognitive-load analysis, dashboard, and API.
• Local-first design: the core signal processing runs locally; the LLM layer is optional and receives summarized outputs rather than raw dense EEG streams.
• Developer-oriented: the goal is to make EEG usable by AI developers, BCI builders, HCI/XR researchers, and open-source hardware communities.

We also wrote a short paper/tutorial explaining the architecture and design choices.

GitHub:
https://github.com/Neuradock/eeg-workstation-agent

Article / tutorial:
https://arxiv.org/html/2606.26518v1

Short demo video:
https://www.youtube.com/shorts/9MWe_rCnWNY

.exe applications:

https://github.com/Neuradock/eeg-workstation-agent/releases/tag/Neuradock_Cognitive_Load_Agent

I’d love feedback from people working on EEG, BCI, neurotech, AI agents, XR/HCI, or adaptive interfaces.

A few questions we’re thinking about:

1. What would be the most useful “killer app” for a real-time cognitive-load API?
2. Would you use this more as a research tool, a developer API, or a hardware demo platform?
3. What integrations would make this more useful: OpenClaw, Claude Code/Codex workflows, Unity, Unreal, browser extension, VS Code, or something else?

Happy to hear criticism too. We’re trying to make EEG more accessible and useful for developers, not just for offline neuroscience analysis.

Hello everyone,

I have been using the 8-channel Cyton headset for the past few months collecting data, cleaning and filtering it in hopes of making a model with about 70 -75% accuracy.

So far my best attempt has been around a 63% F1 score with a limited dataset. As I tried scaling the model with more data the accuracy seems to have been tanked.

I have been struggling quite a bit with the classification as none of the approaches i try seem to work.

The main issue i face is recorded data class separability seems to be very limited.

If anyone has developed a reliable motor imagery model, I would appreciate any tips or guidance.

Thanks

Hello everyone,

I study Computer Science in Germany and work part-time at Agile Robots. I am currently preparing for my bachelor's thesis and, thanks to the support of my company, I have access to several robotic arms.

The goal of my thesis is to control a robotic arm using an EEG system and motor imagery.

At the moment, I am trying to decide which EEG system to purchase and would really appreciate some advice from people with experience in this field.

Can you recommend an EEG system suitable for motor imagery applications?
If you have worked with EEG/BCI systems before, I would also be happy to have a short chat about your experiences.

There are many companies offering EEG systems, but I find it difficult to decide which one to choose. My main concern is investing in a system and later realizing that it does not provide the signal quality required for reliable motor imagery classification.

My ideal budget is around €3–5k, with an absolute maximum of €10k.

Any recommendations, experiences, or suggestions would be greatly appreciated.

Greetings from Germany!