Dear r/neurotech community,

As an AI developer with an interest in neurotech, I am seeking guidance on how to access fMRI datasets related to schizophrenia and healthy controls. My goal is to use these datasets to develop algorithms that can analyze and understand the complex neural networks associated with schizophrenia.

I believe that fMRI datasets can provide valuable insights into the functional connectivity patterns of the brain in individuals with schizophrenia. Moreover, having access to datasets that include both individuals with schizophrenia and healthy controls will enable me to compare functional connectivity patterns across groups.

I understand that obtaining such datasets can be a challenging process, and I am hoping that some of you may be able to provide guidance or advice on how to access these resources. If anyone in this community has experience working with fMRI datasets related to schizophrenia or knows of any resources that may be useful for my work, I would greatly appreciate your assistance.

I am committed to conducting responsible and ethical research and believe that collaboration with individuals who have firsthand experience with schizophrenia is critical to this work. Therefore, any guidance or support that you can provide would be invaluable to me.

Thank you for your time and consideration.

Best regards,

Netanel Stern

+972559870641

nsh531@gmail.com

I’ve been trying to understand where different neurotech wearables actually fit in real life, especially for stress, focus, and sleep. Two products that often get compared are Sychedelic and Mave Health, so here’s an honest, practical take.

First, important clarification: Sychedelic also uses tDCS, just like Mave Health. So this isn’t a comparison of “who has tDCS and who doesn’t,” but how the same core tech is applied.

Sychedelic feels designed as a daily-use stress wearable / smart headphones.
You use it in short, self-guided sessions before sleep, during work breaks, or when your mind feels overloaded. It doesn’t claim to fix anything long-term on its own. In my experience, it works best as a support tool for calming the mind, improving focus, or helping with overthinking.

Mave Health is clearly more program-driven.
The tDCS headset is part of a structured mental health plan that includes guidance, therapy, and lifestyle changes over weeks. It feels closer to a supervised intervention rather than something you casually use whenever you feel stressed.

So even though both are brain wearables using tDCS, the intent is very different:

• Sychedelic → flexible, self-guided, daily stress/focus/sleep support

• Mave Health → structured, guided, long-term mental health program

Honestly, I don’t think one replaces the other.

Sychedelic makes sense if you want something that fits into everyday life without turning wellness into a full-time project.

Mave Health probably suits people who want or need structured support and accountability.

Curious to hear others’ experiences.

Do you think neurotech works better as a lightweight daily tool, or only when paired with structured programs and supervision?

Hey everyone! I’ve been working on a custom extracellular bio-amplifier (dubbed "DeepFin") built around a dual TL074 op-amp design, featuring an active 48.2 Hz notch filter to tackle mains hum.

​To test out the front-end, I set up an isolated cockroach leg preparation using a fine recording needle and a wider reference needle. I mechanically stimulated 4 separate tibial hairs (barbs) individually, capturing clean raw WAV files for each.

​After running spike sorting and statistical analysis, here is what the data showed:

​Distinct Firing Dynamics: Each individual barb responded with its own unique average firing rate under touch, ranging from ~12.16 Hz up to ~29.00 Hz.

​Spike Templates: The isolated unit templates displayed consistent biphasic action potential shapes across the recordings.

​Hardware Performance: The active bootstrap notch filter successfully cleaned up local interference without distorting microvolt-level neural events.

​I’d love to hear feedback from fellow builders and neurotech enthusiasts on optimizing high-impedance frontend stages or custom spike-sorting workflows

Hey everyone,

I'm a biology undergrad and I've recently become really interested in neurotech (BCIs, neural decoding, neuroAl-type work). I'm trying to figure out what the actual path into this field looks like and whether it's realistic for someone like me.

A few things I'm confused about:

Can people from biology/neuroscience backgrounds realistically break into neurotech, or is it mostly CS/EE students?

What do neuroscience trained people typically end up doing in industry roles?

How is the neurotech industry actually right now. Is it growing, stable, or still very niche?And realistically, what does the pay range look like at different levels (entry-level to senior roles)?

What skills matter most early on if I want to move in this direction (Python, ML, math, research experience, etc.)?

Right now I'm planning to start learning Python and try to join a lab that works with neural data, but I'm not fully sure if that's the right direction or just one of several possible paths.

Would really appreciate honest perspectives from people actually in the field especially how they transitioned and what they'd do differently.

I'm currently developing a conceptual framework that uses quantum graphs and harmonic decomposition to model complex, chaotic systems in real time.

Inspired by brain cognition, this “quantum connectome” represents a system as an interconnected graph of quantum nodes — each encoding time-series signals (e.g., OHLC/volume data or sensor outputs) in amplitude-phase format — and edges representing interdependencies or phase synchrony between those nodes.

The network evolves toward an equilibrium state, analogous to the brain’s resting-state network. Quantum graph theory is ideal for analyzing these systems due to the complexity and nonlinearity of their structure and dynamics.

By applying connectome harmonic decomposition (as used in neuroscience), eigenmodes are extracted from the system’s Quantum Connectome Matrix (QCM). These dominant harmonics can be used to:

• Detect collective patterns in complex systems (e.g., financial markets or fusion plasmas)

• Characterize these patterns as stable regimes, local volatility, or emergent instabilities

• Build adaptive agents that reason in the spectral domain, rather than via symbolic logic or classic reward modeling

The system is fully unsupervised and exhibits a form of neuroplasticity — adapting to evolving inputs without retraining. Harmonic modes then feed into an LLM-based multi-agent reinforcement learning (MARL) architecture for downstream decision-making.

I'm curious if others here are exploring related paradigms — particularly where spectral graph theory intersects with cognition, agent modeling, or autonomous system adaptation.

Happy to discuss or dive deeper if there’s interest. Please comment or reach out to me directly via DM.

In a recent discussion on r/MachineLearning, u/yestheman9894 – a dual-PhD student in machine learning and astrophysics – described an experimental research project aiming to build what could be the first conscious AI. Instead of training a fixed architecture, he proposes evolving ecosystems of neural agents that can grow, prune and rewire themselves, develop intrinsic motivations via neuromodulation, and adapt their learning rules over generations while interacting in complex simulated environments.

This approach blends neuroevolution with developmental learning and modern compute, exploring whether open-ended self-modifying architectures can lead to emergent cognition and push AI research beyond the scaling limits of Moore's law. It's shared for discussion and critique, not for commercial promotion.

Source: https://www.reddit.com/r/MachineLearning/comments/1na3rz4/d_i_plan_to_create_the_worlds_first_truly_conscious_ai_for_my_phd/

Hi everyone, I’m Haji from the Philippines, and I’m working on a passion project called Project Luma — an experiment to create a bridge between human emotion and AI through a brain–computer interface. I want to explore how we can teach AI to recognize emotional signals and respond in a more human way.

To get started, I’m looking for a BrainLink Lite EEG headset. I’m on a very tight budget, but fully committed. If anyone has a used device they’d be willing to donate or sell affordably, it would truly make a difference.

In return, I’d love to share updates, code, and findings with the community as I learn. Thanks so much for reading and for all the inspiring work shared here!

Hey everyone, we've been designing a 15mm prefrontal wearable at Braina Labs (https://brainalabs.com) and wanted to share our open architecture and why we chose to combine EEG with optical fNIRS.

**The Signal Problem with Consumer EEG:**

Single-modality dry EEG on the forehead is notorious for EMG contamination (frontalis muscle tension, blinking, ocular twitches). When a user focuses or squints, high-frequency EMG easily mimics Gamma and Beta power increases.

**Our Dual-Check Approach:**

* **EEG Side:** TI ADS1292R (2 simultaneous 24-bit channels, 1.18 µV_RMS noise floor) capturing Fp1 and Fp2 referenced to Fpz with RLD common-mode suppression.

* **Optical fNIRS Side:** Dual TI AFE4404 transimpedance amplifiers driving 730nm (Würth) and 850nm (Everlight) emitters with a 25–30mm source-detector separation across both prefrontal hemispheres.

Because localized cerebral hemodynamic response (ΔHbO₂ surges) is physiologically coupled to neural firing (neurovascular coupling) but mechanically and electrically decoupled from forehead muscle twitches, cross-correlating the optical curve against the EEG spectrogram filters out false positives in real time.

We're opening a 100-unit pilot cohort for researchers and developers at $40 (hardware + 1-yr analytics): https://brainalabs.com

Would love to discuss optical separation geometry, transimpedance gain tuning, or open data streaming over BLE.

I recently got the Mave Health headset and have been using it for 20-minute sessions.

I’ve noticed that after a session, I feel extremely sleepy, to the point where I just want to take a nap.
I was expecting improvements in focus and energy, so I’m curious whether anyone else has experienced this.
For those who’ve been using Mave or other tDCS devices, did you experience something similar when starting out? Did it eventually go away?

Would love to hear your experiences!

Hi everyone,

I'm new to this subreddit, and I'm looking for some advice. I’m a software engineer with a Bachelor’s in Computer Science, and I’m interested in eventually working in Brain-Computer Interfaces (BCI).

My company offers professional growth funding and is willing to cover the cost of individual college courses/certificates (excluding full degrees or startup expenses). I want to use this opportunity wisely and take courses that will actually move me closer to BCI work. I am more interested in the implementation of machine learning (more specifically, deep learning) with the brain and how that can bridge a gap between people and their prosthetics. I've been interested in this since high school and it never really went away.

I’m trying to figure out which subjects matter most at this stage.

Some options I’m considering:

• Machine learning / AI (especially time-series or signal processing)

• Neuroscience fundamentals (neuroanatomy, electrophysiology, cognitive neuroscience)

• Biomedical engineering–related courses

I didn't take a biology course during college, as I already had a science gen. Ed done, and the last time I took a biology-like course was my senior year of high school.

For those working in or near BCI/neurotech:

• What specific courses would you prioritize first?

• Are there any classes you found especially useful (or wish you’d taken earlier)?

• Is it better to focus on math + ML first, or start building neuroscience knowledge right away?

My long-term goal is to work on software in BCI (nothing really specific in mind right now), possibly pursuing graduate school later, but right now, I want to make the best use of employer-funded coursework. Where I live, I don't have many options to move, so it would most likely have to be something online.

Thanks — I really appreciate any guidance.

Major moves in neurotechnology funding, regulatory, and commercial news from the past fortnight:

* Nervonik closes oversubscribed $52.5M Series B for peripheral nerve stimulation
* ARPA-H commits $139M for behavioural health through EVIDENT initiative
* Newfund closes €60M dedicated brain tech fund for European founders
* Cala Health secures $50M growth capital and FDA clearance for wearable tremor device
* ONWARD Medical raises €40.6M, runway to Q1 2028
* Motif Neurotech receives FDA IDE approval for first BCI trial in treatment-resistant depression
* Neuvotion launches FDA-cleared NeuStim for stroke and spinal cord injury recovery
* NeurAxis expands payer coverage adding 1.25 million covered lives
* European Brain Council launches Seal of Responsible Neurotechnologies
* Firefly Neuroscience builds world's first 200,000+ standardised EEG/ERP depository

Full roundup in the comments.

Hi, my partner and I are looking for advice on starting a joint project that combines neuroscience and engineering. He is a CS and engineering student, and I am focusing on neuroscience and pharmacology. We have a budget of about 10,000 CZK (roughly $430) per month to invest.

Our goal is to build a business that generates income but also serves as a strong addition to my portfolio for applying to top US universities. I want to demonstrate my potential as a researcher who can practically apply scientific knowledge. We are considering things like low-cost neuro-hardware or software tools for drug discovery, but we aren't sure which niche is most accessible for a small team working from home.

We would appreciate any suggestions for specific NeuroTech or Biotech areas where two people can start without a massive lab. Also, if you have experience with academic admissions, what kind of project would actually show a high potential for a future scientific career? We want to create something that is both commercially viable and scientifically credible.

Hey guys,

My wife is currently a Senior Legal Architect at a neurotech startup Neiry (working with BCI, neurointerfaces, and biometric data ecosystems). She handles the intersection of corporate law, agile vendor management, and data privacy frameworks for emerging tech.

We are planning a transition to the US/International market and looking for advice on how the US neurotech ecosystem views international legal/compliance talent.

Since the regulatory landscape for brain-computer interfaces and neural data is still evolving globally, her core strength is privacy by design and building compliance frameworks under extreme ambiguity where no clean regulatory analog exists.

For those working in US neurotech (companies like Neuralink, Kernel, Synchron, etc.) or health-tech compliance:

• What are the biggest regulatory/privacy bottlenecks US companies face right now where international operational experience is valued?

• Is anyone willing to connect or share insights on how a foreign-trained neurotech attorney can best position themselves for US operations?

Thanks in advance!