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Sergey Musienko
CEO
Build, Automate, & Personalize
Brain-Computer
Interfaces.
Visual BCI IDE, autonomous research agents, and sub-20ms local runtime.
A Complete Platform for Modern Neurotechnology
From visual experiment design to autonomous agentic sweeps and on-device adaptation.
Graphical BCI IDE
Nimbus Studio
Visual Canvas & AI Generation
Visual node graph with 7 foundation backbones (REVE, LaBraM), 30+ headsets, and 1-click Python export.
Agentic BCI
Nimbus MCP
Autonomous Research for AI Agents
58 native tools connecting Claude Code and Cursor to 90+ datasets, sweeps, and BIDS exports.
Adaptive Intelligence
Personalizer & Conductor
Adaptive Layer in SDK & Studio
Freeze foundation models. Adapt a calibrated Bayesian head on-device in <20ms without retraining.
Developer Engine
Nimbus SDK
Sub-20ms Local Reactive Runtime
Sub-20ms Python and Julia runtimes with 100% private, on-device neural processing.
Visual BCI IDE
Design, Simulate, and Deploy in Minutes
Design pipelines in natural language, benchmark 7 foundation models, and export production Python with one click.
Create with AI
Prompt-to-pipeline generation. Speak your paradigm and get a runnable graph in seconds.
Apps Hub
Zero-calibration feedback apps and interactive games.
30+ EEG Devices
Native streaming for OpenBCI, Muse, and g.tec with live impedance.
1-Click Python Export
Clean, reproducible scikit-learn code with zero vendor lock-in.
Agentic BCI · Open Source
Let AI Agents Drive Your BCI Research
Connect Claude Code, Cursor, and Codex directly to 90+ datasets, benchmark sweeps, and BIDS exports.
Claude Code & Cursor — nimbus-mcp
foundation.compare❯ Compare EEG foundation models on BNCI2014-001 against EEGNet→ nimbus.data.inspect_dataset("BNCI2014-001", subject=3) ↳ 22ch EEG · Motor Imagery (4 classes) · 288 trials @ 250Hz→ nimbus.foundation.compare(backbones=["reve", "labram", "eegpt"]) ↳ Evaluated 3 backbones with frozen embeddings (100-trial budget)→ nimbus.experiment.run(pipeline="reve_bayesian_head", baseline="eegnet") ↳ 5-fold cross-validation complete · sub-20ms latency verified✓ Best: REVE + Bayesian Head achieved 78.4% ± 2.1% (+7.2% vs EEGNet)✓ Interactive pipeline saved to Studio · BIDS dataset exported58 Specialized Tools
Dataset discovery, foundation model benchmarks, parameter sweeps, and BIDS export.
Autonomous Campaigns
Run up to 25 experiment configurations and aggregate cross-validation metrics in one prompt.
Confirm-Gated Safety
Hardware-touching calls require explicit human consent with idle watchdogs.
Same accuracy band · far lower update cost
Head updates ~23× cheaper than fine-tuning the trunk
# Python SDK (nimbus-bci)
from nimbus_bci import Personalizer
# Freeze encoder, adapt Bayesian head
clf = Personalizer(encoder=frozen_trunk, head="bayesian")
clf.partial_fit(new_session_eeg) # <20ms on device
Nimbus SDK · Adaptive Intelligence
Freeze the trunk. Personalize the head.
Universal transfer-learning middleware in Python (nimbus-bci) and Julia (NimbusSDK.jl)
Eliminate calibration fatigue. Freeze large neural encoders (REVE, LaBraM, EEGNet) and adapt a calibrated Bayesian head on-device in <20ms, governed by Conductor.
Governed by Conductor
Runtime drift diagnosis, label spend bounds, and ShadowGuard safety bounds.
Universal BCI Personalization: One API for Frozen EEG Trunks … · arXiv:2607.22397
Developer Runtime
The Sub-20ms Engine. Built for Developers.
All inference and adaptation run locally on your machine with zero cloud roundtrips. Compatible with scikit-learn, MNE-Python, and PyRiemann, with high-performance Julia support.
bci_pipeline.py
# Install via pip
$ pip install nimbus-bci
from nimbus_bci import Personalizer, NimbusLDA
# 1. Adapt a frozen encoder to a new user (<20ms local update)
clf = Personalizer(encoder=frozen_trunk, head="bayesian")
clf.partial_fit(calibration_eeg, labels)
# 2. Real-time streaming inference with calibrated uncertainty
for epoch in live_eeg_stream:
proba, entropy = clf.predict_proba(epoch, return_entropy=True)
if entropy < 0.25:
trigger_action(proba.argmax())End-to-End Processing Latency10x Faster
Standard BCI Processing200ms+ Latency
Nimbus SDK<20ms Latency
Sub-20ms Latency
10x lower latency than traditional deep learning pipelines for true real-time neurofeedback and closed-loop control.
100% Local & Private
Zero neural telemetry leaves your device during inference. Private, compliant, and fully offline-capable.
Online Personalization & sklearn API
Adaptive Personalizer middleware with partial_fit() interfaces, full posterior distributions, and calibrated uncertainty metrics.
Built on a Foundation of Proven Technology
The Nimbus engine is powered by RxInfer, the state-of-the-art reactive message passing framework for automated Bayesian inference. In partnership with Lazy Dynamics, the core developers of the technology, we are building the most performant and reliable software layer for the BCI industry.
AI that can't handle uncertainty, can't handle reality.
Typical BCIs fire false actions during blinks or noise. Nimbus models calibrated Bayesian uncertainty to safely suppress erratic outputs.
See how uncertainty drives better decisions
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Current AI
single prediction
Probabilistic AI
prediction distribution
Built for Neuro-Innovators by Deep Tech Experts
Our purpose-built team combines decades of experience in scaling deep tech ventures, core probabilistic programming, and neuroscience. We've built the engine because we've lived the problem.
Albert Podusenko
CTO
Kirill Parinov
CBDO
Alex Sidorov
Chief Revenue Officer
Sergey Musienko
CEO
Albert Podusenko
CTO
Kirill Parinov
CBDO
Alex Sidorov
Chief Revenue Officer
Sergey Musienko
CEO
Albert Podusenko
CTO
Kirill Parinov
CBDO
Alex Sidorov
Chief Revenue Officer
Sergey Musienko
CEO
Albert Podusenko
CTO
Kirill Parinov
CBDO
Alex Sidorov
Chief Revenue Officer
What Our Partners Are Saying
"Nimbus is bringing the power of reactive Bayesian inference to brain-computer interfaces. Their software layer solves the critical bottleneck of real-time adaptation that has held the industry back for decades."
— Dmitry Morozov, Co-founder, Lazy Dynamics · Research Partner, TU Eindhoven
Ready to build the future of neurotechnology?
See a live demonstration of the Nimbus engine and discuss how our technology can accelerate your BCI development.