Here I am using motor imagery alone to move a red cursor in one dimension (left / right). While I am using an OpenBCI Cyton + Daisy, I'm using mainly C3, Cz, and C4 (along with a few other channels)

Four dimensions next!

tldr: upload raw EEG, run a pipeline, get cleaned signals + figures + exports. first 100 accounts get 10 free files.

context first: i'm a software dev, not an EEG researcher. what i kept running into was the same complaint in different words — people fighting MNE scripts, montages and broken pipelines, or quoting enterprise prices for what looked like a handful of filters and a plot. the missing piece seemed to be decent software, not more science, so i built it.

mzreeg.com — upload (edf/bdf/fif/set/brainvision/cnt/gdf), pick a preset like "standard clean" or "deep clean (blinks + heartbeat)", tweak sliders if u want, hit run.

i leaned on the libraries people already trust instead of rolling my own dsp: mne for io/filter/epoch/evoked/psd/tfr, autoreject for trial rejection, pyprep (PREP/NoisyChannels) for bad channels, mne-icalabel to label ICA comps so blink/heart get auto-dropped, meegkit for ASR, mne-connectivity for wPLI/PLV, specparam (FOOOF), pycrostates for microstates, sklearn for CSP+LDA decoding. u get cleaned signals, psd/tfr/connectivity/decode plots, bad-channel review, png/html figures, exports in fif/edf/set/csv/npy, and a provenance json with the exact parameters of every run.

honest bits: i don't have a neuro background, so the gaps are probably in the science rather than the plumbing. it's file in, results out — no streaming, single subject, no scripting API yet. perf is mid: i can't afford always-on hosting, so the worker sleeps and the first run after idle takes 30-60s to warm up, then ~10s for a 60s recording through 5 steps. once this hits ~$100 MRR i'll move it to always-on and that goes away. not a medical device, research use only.

first 100 accounts get 10 free files, no card.

so if u actually process EEG: where is this wrong, or useless? what would u need to drop ur current script for it? and if u know the methods — is auto-dropping icalabel components too aggressive? would u trust a wPLI that somebody else's pipeline computed?
Update: Added more info to the site, and in a reply below, tldr: data is (now) deleted after 24h. No data is shared.

Greetings, I am looking to connect with BCI enthusiasts who are not afraid to get their hands dirty. Whether you work with EEG data, normalizers, translators, hardware/bioware, there are a bunch of projects that could use your expertise.

Happy to share more in dms, but to give you some hints, our research revolves around:

- installable neural memories/capabilities, artificial memories, and knowledge bundles via BCI.

- thought security/neurosecurity protocol.

- operating systems and meta-ui deriving from XR

We made a short video about brain-computer interfaces and how they're already doing things like helping people with paralysis control robotic arms. But would you trust a company to develop technology that uses signals from your brain? What safeguards would you want in place?

Hi, everyone, I would like to share a 24-channel EEG system using commercially available ADS1299 boards and an FPGA.

Hardware Architecture：

1. ADC Front-End: 3x commercial TI ADS1299 boards.
2. Controller: FPGA(ZYNQ7010). I built 3 independent SPI interfaces in order to control ADS1299 simultaneously.
3. Transmission: Direct UDP packet streaming to the PC.

The Results：

1. Demo video https://youtu.be/qYt3QFGQV-o

Next Steps：

1. My next step is to reconfigure this 24-channel, single-user setup into an 8-channel × 3-user architecture for simultaneous multi-person EEG streaming.
2. I plan to push the sampling rate up to 2000 SPS. The current 24-ch @ 1000 SPS seems ok, and I've already verified 2000 SPS across all channels using the ADS1299's internal test signals.
3. ZYNQ7010 still has PL resources and I/O pins left. I plan to build 3 additional parallel SPI interfaces, doubling to 48 channels.

Feel free to discuss.

I I need the V2K app for Android please my nr is +27640140566 Whatsapp

I am senior sw engineer with experience in math and scientific computing. I would like to participate in BCI research or develop software for BCI. I see no established companies hiring.

I have experience with neurofeedback and EEG in general.

I already worked on commercial neurofeedback project.

I can work parttime or on project basis. I am positioned in Eastern Europe.

# Sleep-State Neural Synchronization Architecture: A Distributed Cloud Framework for Multi-User Virtual Reality and Mental Simulation

Date: October 2026 Author: Lucian Altheris

### Abstract Current approaches to Full-Dive Virtual Reality (VR) face significant physical and computational hurdles, primarily due to the immense bandwidth required to stimulate peripheral nervous systems and the resulting cognitive overload on biological neural networks. This paper proposes an alternative system architecture: a cloud-based, distributed Brain-Computer Interface (BCI) framework that offloads heavy computational physics and environment rendering to a central server, while utilizing human sleep-wave baselines (Delta/Theta rhythms) as a standardized, low-noise protocol for direct conceptual data transmission into the human mental workspace.

1. Introduction & Problem Statement

Traditional BCI and neural interface explorations focus heavily on replicating physical sensory modalities (touch, taste, vision via optical stimulation) or reading motor intent. However, these paradigms encounter critical bottlenecks: * **Cognitive Overload:** Continuous processing of high-frequency sensory inputs during waking states causes rapid synaptic fatigue and potential neural stress. * **Encoding Heterogeneity:** Waking human brainwaves exhibit high individual variance, complex emotional noise, and distinct cognitive patterns, making standardized data decoding extremely difficult. * **Hardware Limitations:** Present-day silicon processors and local client architectures lack the capacity to simultaneously render real-time multi-sensory realities and decode complex human neural signals.

2. Proposed System Architecture

To bypass physical sensory limitations and high cognitive friction, we propose a **Distributed Cloud Neural Architecture** consisting of three primary layers:

A. Centralized Server Offloading (Core Engine)

* **Function:** The central server acts as the primary compute engine, managing environmental physics, logic rules, and spatial data generation. * **Advantage:** By offloading computation from the user, biological neural tissues are spared from high processing loads, preventing neural overheating and prolonged mental fatigue.

B. Sleep-Wave Baseline Protocol ("Safe-Boot" Mechanism)

* **Function:** Instead of attempting data injection during high-noise waking states, the system utilizes **sleep-wave baselines (Delta and Theta rhythms)** as a universal synchronization window. * **Rationale:** During deep sleep and REM states, neural activity across different human brains aligns into more uniform, highly structured baseline rhythms with minimal emotional and cognitive noise, serving as an optimal "Safe-Boot" protocol for data transmission.

C. Shared Mental Space & Sub-Second Loop

* **Function:** Incoming raw conceptual data streams from the server are fed directly into the user’s internal visual-spatial rendering engine (the mind's eye), allowing the brain to natively construct the virtual environment without physical sensory simulation. * **Multi-User Synchronization:** User decisions and mental states are streamed back to the central server, aggregated, and looped back to all connected participants in sub-second intervals to establish a shared multi-user virtual space.

3. Safety Protocols and Adaptive Bandwidth

To maintain cognitive safety, the architecture incorporates a **Neural Resilience and Dynamic Limit Protocol**: * **Real-Time Monitoring:** The system continuously tracks brainwave stability and stress markers. * **Automated Time-Out & Pacing:** Standard profiles enforce strict connection limits to prevent fatigue. * **Resilience Bypass:** Users exhibiting high neural bandwidth, structural processing efficiency, and low emotional friction are dynamically granted extended connection privileges based on their measured cognitive resilience.

4. Development Roadmap

The realization and testing of this architecture follow a phased engineering approach:

1. **Phase 1 (Animal Model Testing):** Validate safe, non-destructive two-way signal transmission and baseline server-to-neural communication using mammalian subjects during resting states.
2. **Phase 2 (Single-Human Sleep Integration):** Test conceptual data packet injection on a single human subject during controlled sleep-state synchronization.
3. **Phase 3 (Multi-User Expansion):** Scale the architecture to interconnect multiple users within a centralized, server-managed shared conceptual environment.

5. Conclusion

By shifting the paradigm from physical sensory recreation to direct conceptual data streaming via synchronized sleep-wave baselines, this architecture offers a scalable pathway toward high-immersion mental simulation. While current computing hardware and non-invasive BCI bandwidth remain constrained, establishing this theoretical framework provides a clear blueprint for future advancements in neuroengineering and distributed virtual systems.

Hey everyone! I’m 14 years old and working on a conceptual architecture to achieve true Full Dive VR.

The main goal is to allow a user to enter any virtual world or custom environment — for example, entering the world of Naruto, becoming the main character, and living out his story with 100% realistic physical and sensory immersion.

You might ask: "How is this possible? Isn't it sci-fi?"

My proposed design breaks down the system into two independent processing units (chips):

Brain Chip (Motor Cortex): Intercepts and decodes physical movement commands (walking, running, moving arms, controlling the avatar).

Spinal / Sensory Chip: Handles full sensory and emotional input — pain, fear, scent, touch, and visual perception signals.

Why 2 Chips Instead of 1?

By distributing the processing across two dedicated chips, we prevent single-processor overload and heat buildup. Splitting motor control from sensory/emotional feedback keeps hardware performance stable and temperatures low.

How the Brain Simulates Perception:

The brain operates as the central controller ("the boss"), while neurons act as execution agents ("messengers").

Our brains naturally simulate sensation even before physical contact occurs. For instance, if you imagine striking your foot with a hammer, your mind instinctively triggers a sharp, phantom sensation of pain and alarm.

Visual input normally alerts the brain to danger, triggering fear responses. The Spinal/Sensory chip acts as an artificial sensory proxy, feeding decoded signals for pain, scent, and emotional arousal directly into the nervous system.

Safety, Environment & Consciousness:

Life-Support Pod: The user remains inside a specialized pod monitored by an AI system tracking heart rate, respiration, and vital signs.

Motor Inhibition: The body is placed into a deep sleep / medically suspended state to prevent physical movement in the real world while active in VR.

Memory Suspension: Real-world memories are temporarily suppressed during the session to maximize immersion, restoring automatically upon waking up or logging out.

Neuralink has already demonstrated that thought-controlled cursor movement is possible. Expanding this into full-body motor control and sensory feedback is the logical next evolutionary step.

I’m giving myself a 4-year timeline to fully flesh out this framework, study the underlying neurobiology, electronics, and algorithms, and build a complete blueprint.

Feel free to ask questions, share technical critiques, or drop your ideas below — I’ll respond to everyone! 🚀🧠

I am senior sw engineer with experience in math and scientific computing. I would like to participate in BCI research or develop software for BCI. I see no established companies hiring.

I have experience with neurofeedback and EEG in general.

I already worked on commercial neurofeedback project.

I can work parttime or on project basis. I am positioned in Eastern Europe.