Built for teams at every stage.

Challenge

Consumer EEG companies face a fragmented hardware landscape. A model trained on your dev kit might fail on production hardware or competitor devices.

How Hermeneia helps

• Normalize across electrode counts and configurations
• Train on combined data from multiple devices
• Deploy models that maintain accuracy across your product line

Outcome

Faster iteration, broader compatibility, reduced re-training costs.

Challenge

Clinical neuroscience research often works with small, precious datasets. Getting statistical power requires every sample to count.

How Hermeneia helps

• Transfer learning from large non-clinical datasets
• Augmentation that respects physiological constraints
• Embeddings that capture clinically-relevant variation

Outcome

Extract publishable insights from smaller cohorts. Accelerate path from discovery to validation.

Challenge

Neurofeedback products need real-time, reliable signal processing across varied user conditions—different headset fits, hair types, environments.

How Hermeneia helps

• Real-time embedding API for low-latency processing
• Robust normalization that handles everyday noise
• Consistent representations despite session-to-session variation

Outcome

More reliable user experience. Fewer "poor signal" interruptions.

Challenge

Teams building motor-imagery BCIs or communication devices need high accuracy and minimal calibration burden for users with disabilities.

How Hermeneia helps

• Pre-trained motor imagery representations
• Few-shot adaptation to individual users
• Cross-session stability

Outcome

Reduce calibration time. Improve accuracy for users who need it most.

Challenge

Academic labs spend months rebuilding preprocessing pipelines for each new project, device, or dataset.

How Hermeneia helps

• Standardized preprocessing out of the box
• Compatible with major file formats (EDF, BDF, etc.)
• Reproducible, versioned pipelines

Outcome

Focus on science, not plumbing. Faster time to first results.