Neural Decoding Self-Calibration Using Passive Sensor Feedback

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Solution Overview

Problem

Existing brain-computer interfaces require frequent recalibration due to sensor displacement, signal degradation, and neural plasticity, reducing their utility and efficiency.

Innovation Solution

A neural device system with nonpenetrating cortical microelectrodes and external sensors that automatically recalibrate a neural decoding model using passively collected information from inertial, visual, and auditory feedback, minimizing the need for active recalibration.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If frequent recalibration is performed to maintain decoding accuracy, then measurement precision is improved, but loss of time increases

Engineering Contradiction:

Improvedecoding accuracyVSAvoidcalibration time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs automatic recalibration using passively collected sensor data without requiring active user participation. The neural decoding model is self-adjusting by comparing sensor states with decoded outputs and automatically updating parameters, enabling the system to maintain accuracy while minimizing calibration time and user burden.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements a feedback mechanism where sensor data (inertial, visual, auditory) is continuously compared with neural decoding outputs. This feedback loop enables automatic detection of drift and triggers recalibration only when necessary, maintaining decoding accuracy while reducing unnecessary calibration sessions.

Inventive Principle:

Principle #23Feedback

2Object-affected harmful factors

If nonpenetrating cortical microelectrodes are used to reduce invasiveness, then object-affected harmful factors are reduced, but measurement precision deteriorates

Engineering Contradiction:

Improvetissue damageVSAvoidsignal quality

Core Design Contradiction:

Object-affected harmful factorsVSMeasurement precision

Solution Approach 1:

The system combines multiple sensor types (inertial sensors, visual feedback, auditory feedback) into a unified calibration framework. This multi-functional approach compensates for the lower signal quality of nonpenetrating electrodes by integrating diverse data sources, maintaining overall measurement precision while using less invasive electrodes.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system creates a composite measurement approach by combining neural signals with external sensor data (inertial, visual, auditory). This composite methodology compensates for signal degradation from nonpenetrating electrodes, maintaining measurement precision while reducing tissue damage.

Inventive Principle:

Principle #40Composite materials

Data Source

PatentUS12530080B2Systems and methods for self-calibrating neural decoding

Publication Date: 2026.01.20 PRECISION NEUROSCIENCE CORP

AI summary

Systems and methods related to recalibrating neural devices based on external sensory information are disclosed. The systems can include a neural device configured to sense data associated with the subject or receive control input, a sensor communicably coupled to the external device, the sensor configured to detect a state associated with the subject, and an external device communicably coupled to the neural device and the sensor. The external device can recalibrate a neural decoding model based on the state detected via the sensor, wherein the neural decoding model correlates the data received from the neural device with a corresponding thought or action performed by the subject.