ML Infrastructure for Neural Signals
Better models.
Less data.
Any device.
Hermeneia provides pre-trained representations and device-agnostic normalization that let your team train production-quality brain-signal models without massive annotation budgets or hardware lock-in.
The Challenge
Neural ML is stuck on three problems.
Annotation economics
Labeling brain-signal data requires expert hours that don't scale. Most teams hit diminishing returns before reaching production quality.
Device fragmentation
A model trained on one EEG headset fails on another. Your work becomes locked to a single vendor's hardware.
Cold-start overhead
Every new project starts from scratch—no transfer learning, no shared representations, no accumulated advantage.
Our Approach
Steepen your cost-performance slope.
Traditional neural ML has a shallow slope—doubling your data investment might yield 10% better performance. Hermeneia's pre-trained representations change the curve fundamentally.
With shared representations, your first 1,000 labeled samples achieve what typically requires 10,000.
Device Agnostic
Train once.
Deploy on any hardware.
Our normalization layer abstracts away electrode configurations, sampling rates, and device-specific artifacts. Your model sees a unified signal representation regardless of the recording hardware.
- —Spatial interpolation handles different electrode counts
- —Temporal normalization aligns varying sample rates
- —Artifact signatures are device-normalized
- —Transfer learning works across manufacturers
Applications
Where teams use Hermeneia.
Consumer BCI
Ship consumer-grade accuracy without research-grade data requirements.
Clinical Research
Accelerate trial analysis with pre-trained biomarker detection.
Neurofeedback
Real-time signal classification with 50ms latency targets.
Assistive Technology
Robust intent decoding that works across user variability.
Integration
From raw signal to trained model.
Connect
Your existing data pipeline sends signals to our SDK.
Normalize
Device-agnostic preprocessing creates unified representations.
Embed
Pre-trained models generate rich feature vectors.
Train
Your task-specific model trains on embeddings, not raw signals.
Deployment
Built for teams with real requirements.
Data residency
Your data can stay in your environment. Our cloud APIs process in isolated pipelines and delete after computation.
No training on your data
We never use customer data to improve our models without explicit written consent.
Compliance-ready architecture
Designed to support teams pursuing HIPAA, GDPR, and SOC 2 compliance. We provide BAAs on request.
Not a medical device
Hermeneia is development infrastructure. Regulatory responsibility for clinical claims rests with deploying organizations.
Start building on better foundations.
Most teams run their first pipeline within a day. We'll help you understand if Hermeneia fits your stack.
Request AccessOr email us directly: hello@hermeneia.info