Neural Decoder 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 neural decoding models using passively collected data from inertial, visual, and auditory feedback, minimizing the need for active recalibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brain-computer interfaces use frequent recalibration to maintain accuracy, then decoding precision is improved, but system productivity and user time efficiency deteriorate due to lengthy calibration sessions
Solution Approach 1:
The system performs preliminary calibration to establish a baseline decoding model, then uses passive monitoring to automatically detect when recalibration is needed based on drift thresholds, avoiding frequent manual recalibration sessions while maintaining accuracy
Solution Approach 2:
The system continuously monitors neural signal drift and compares it against predefined thresholds, providing feedback that triggers automatic recalibration only when necessary, thereby maintaining decoding accuracy without requiring frequent manual intervention
2Measurement precision
If brain-computer interfaces use frequent recalibration to maintain accuracy, then decoding precision is improved, but loss of time increases due to extensive training requirements
Solution Approach 1:
The system performs preliminary calibration to establish a baseline decoding model, then uses passive monitoring to automatically detect when recalibration is needed based on drift thresholds, avoiding frequent manual recalibration sessions while maintaining accuracy
Solution Approach 2:
The system automatically monitors its own performance and triggers recalibration when drift exceeds thresholds, eliminating the need for users to manually initiate lengthy calibration sessions and reducing overall time investment
3Measurement precision
If brain-penetrating microelectrode arrays are used to achieve high-spatial-resolution recordings, then signal quality is improved, but tissue damage and invasiveness increase
Solution Approach 1:
The patent extracts the electrode array from the brain tissue by using non-penetrating cortical surface electrodes instead of penetrating microelectrodes, thereby maintaining high-spatial-resolution recordings while eliminating tissue damage and invasiveness associated with penetration
Solution Approach 2:
The system uses non-penetrating cortical surface electrodes that capture neural signals from the brain surface, providing a non-invasive copy of the neural activity that would otherwise require penetrating electrodes to obtain with high spatial resolution
Data Source
AI summary
Systems and methods related to recalibrating a neural decoding model are disclosed. The method can include recording a plurality of time-synced signals from a neural device and a sensor; extracting features from the plurality of time-synced signals, the features relating to a known action; retraining the neural decoding model on the extracted features; outputting a prediction on a probability of the known action occurring using the retrained neural decoding model; and determining whether the prediction from the retrained neural decoding model corresponds to the known action according to a predefined quality threshold.