Universal Parametric Calibration Front-End for Brain–Computer Interfaces (P300 & SSVEP)

Indicative scope: method + system + software for parametric probing, tuning-map construction, algorithmic selection of parameter regions/features for decoders; optional tuning-map database and transfer-learning acceleration. Full claim scope and implementation details are available under NDA.

Teams building P300 and/or SSVEP BCIs who want more reliable, faster onboarding than fixed “preset” calibration. The approach is designed for production constraints: repeatability, robustness, and integration with existing decoders.

The invention adds a distinct architectural layer to stimulus-evoked BCI systems: a parametric calibration front-end that (i) varies stimulus parameters over time, (ii) measures neural response features, and (iii) builds an individualized tuning map linking parameters × neurofeatures. The front-end then outputs a compact configuration (selected parameter regions + feature set) used by one or more decoders.

• Parametric probing: sweep/modulate ≥1 stimulus parameter (optionally ≥2) during calibration.
• Tuning map: individualized response profile over parameters × extracted features.
• Algorithmic selection: choose robust operating regions (“hot spots”) and feature sets for decoding.
• Reusable front-end: selected features can feed multiple decoders within the same paradigm family (P300/SSVEP) without repeating the full sweep.

Many production BCI pipelines rely on a small set of fixed presets (e.g., a few frequencies / timings) and a narrow evaluation metric. This can create brittle performance: a user works well only for a narrow setting, or performance drifts across sessions.

• Preset-based calibration can miss narrow maxima (small parameter changes can materially affect response strength and separability).
• Manual tuning is slow and hard to scale across users, devices, and sessions.
• Calibration data is often under-utilized (no unified map that can be re-used to set up decoding).

A systematic, reusable calibration layer that explicitly models how responses vary with stimulus parameters and uses that structure to select decoding inputs—rather than relying on static presets.

• Parameter–feature coupling: build an individualized map over stimulus parameters × extracted neural features.
• Quality scoring: evaluate candidate regions/features with objective criteria (e.g., response strength, stability, separability).
• Robust selection: pick operating regions and feature sets that remain stable across trials/sessions.
• Exportable configuration: generate a single feature/parameter bundle consumable by decoders (software-defined front-end).
• Optional acceleration: use a tuning-map database + transfer learning to prioritize promising regions for new users.

• Shorter calibration (focus probing on informative regions; fewer “wasted” trials).
• Higher personalization (user-specific tuning profile; easier re-calibration over time).
• More robust decoding (less sensitivity to brittle preset choices).
• Better data economics (a structured map enables reuse and systematic selection).
• Scalable deployment (on-device, cloud, or hybrid pipelines).

Note: This page intentionally avoids enabling implementation specifics (exact feature sets, scoring functions, parameter schedules, and validation results). Those materials are shared under NDA.