Platform
The infrastructure layer for brain-signal ML.
Pre-trained representations, cross-device normalization, and efficient training—between your raw data and your models.
Request a DemoCapabilities
What Hermeneia does
Pre-trained embeddings
Foundation models trained on diverse, ethically-sourced brain-signal datasets. Fine-tune for your application with minimal data.
Cross-device normalization
Automatic preprocessing that handles electrode mapping, sampling rate conversion, and hardware-specific noise patterns.
Intelligent augmentation
Domain-aware data augmentation that creates realistic training variations without introducing implausible patterns.
Transfer learning toolkit
Tools for efficiently adapting pre-trained representations to new tasks, devices, and patient populations.
Embedding API
Simple REST and Python APIs to generate embeddings for your neural data in real-time or batch.
Evaluation framework
Standardized benchmarks to measure model performance, generalization, and robustness before deployment.
Architecture
How it fits your stack
Hermeneia integrates at the preprocessing and representation layer—above raw signal acquisition, below your application logic.
Integration
Built for your workflow
Python SDK
Pip-installable library for Jupyter notebooks, training pipelines, and research environments.
REST API
Cloud-hosted endpoints for production inference and batch processing.
On-premise
Docker-based deployment for teams with strict data residency requirements.