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Dr Alexandra Bernadotte
CEO · Co-founder
Build, Automate, & Personalize
Brain-Computer
Interfaces.
Visual BCI IDE, autonomous research agents, and sub-20ms local runtime.
A Complete Platform for Modern Neurotechnology
Nimbus Studio
Visual Canvas & AI Generation
Visual node graph with 7 foundation backbones, 30+ headsets, and 1-click Python export.
Nimbus MCP
Autonomous Research for AI Agents
58 native tools connecting Claude Code and Cursor to 90+ datasets, sweeps, and BIDS exports.
Personalizer
Adaptive Layer & Conductor
Freeze backbones and adapt a calibrated Bayesian head on-device in <20ms without retraining.
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.
❯ Benchmark foundation models on BNCI2014-001 and plot leaderboard→ nimbus.foundation.compare(backbones=["reve", "labram"], baseline="eegnet")→ nimbus.plots.leaderboard(dataset="BNCI2014-001", metric="accuracy") ↳ REVE + Bayes ████████████████████ 78.4% ± 2.1% [best] ↳ LaBraM ████████████████░░░░ 74.1% ± 2.6% ↳ EEGNet (base) ██████████████░░░░░░ 71.2% ± 3.0%✓ Leaderboard figure exported: artifacts/bnci2014_leaderboard.pngSame 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 founding team combines decades of experience in neuroscience, medicine, mathematics, and scaling deep tech ventures. We've built the engine because we've lived the problem.
Ivan Menshikov
CTO · Co-founder
Sergey Musienko
CPO · Co-founder
Dr Alexandra Bernadotte
CEO · Co-founder
Ivan Menshikov
CTO · Co-founder
Sergey Musienko
CPO · Co-founder
Dr Alexandra Bernadotte
CEO · Co-founder
Ivan Menshikov
CTO · Co-founder
Sergey Musienko
CPO · Co-founder
Dr Alexandra Bernadotte
CEO · Co-founder
Ivan Menshikov
CTO · Co-founder
Sergey Musienko
CPO · Co-founder
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."
— Albert Podusenko, 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.