Direct Neural Interface Calibration with Forget-Factor Model Updates
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Solution Overview
Problem
Direct neural interfaces face challenges in maintaining the validity of predictive models due to brain variability, requiring frequent updates and significant data manipulation, which is computationally intensive and time-consuming.
Innovation Solution
A method that involves acquiring input and output calibration tensors, calculating covariance and cross-covariance tensors, and applying partial least squares multivariate regression with a forget factor to update the predictive model, allowing for efficient recalibration and reducing computational power and time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frequent calibration updates are performed to maintain model validity due to brain variability, then model accuracy is maintained, but computational time and processing resources increase significantly
Solution Approach 1:
The patent segments the calibration process by separating new calibration data from historical calibration data. Instead of processing all calibration data together, the system processes only new data segments while maintaining a repository of historical calibration tensors. This segmentation reduces the computational burden of each update while preserving the benefits of accumulated calibration information through selective integration of historical data.
Solution Approach 2:
The patent changes the parameter of data volume processed during calibration updates. By adjusting the amount of historical calibration data integrated (controlled by parameter alpha) and the recency weighting (controlled by parameter beta), the system can balance between using more data for accuracy and using less data for faster processing. This parameter adjustment allows flexible trade-offs between model validity and update duration.
2Measurement precision
If complete calibration data sets are processed for model updates, then calibration accuracy is improved, but computational power requirements increase
Solution Approach 1:
The patent extracts and stores calibration information in a compressed tensor format that preserves essential calibration characteristics while reducing data volume. The calibration tensor repository stores pre-processed calibration information that can be selectively applied during updates, eliminating the need to reprocess complete raw calibration data sets during each model update, thus reducing computational power requirements while maintaining calibration accuracy.
Solution Approach 2:
The patent applies partial calibration updates by integrating only a weighted portion of historical calibration data rather than processing complete data sets. The integration parameters (alpha and beta) control the extent of historical data usage, allowing the system to achieve sufficient calibration accuracy with partial data processing, thereby reducing computational power requirements while maintaining adequate calibration precision.
3Reliability
If regular calibration phases are implemented to account for brain variability, then model reliability is maintained, but the frequency of data acquisition and processing increases
Solution Approach 1:
The patent performs preliminary organization of calibration data into tensor format and stores it in a repository during initial calibration phases. This preliminary action prepares calibration information in advance for efficient retrieval and integration during model updates, reducing the processing workload during regular calibration phases and improving overall update efficiency while maintaining model reliability.
Solution Approach 2:
The patent implements continuous calibration by maintaining a repository of calibration tensors that can be incrementally integrated into the predictive model over time. Instead of discrete, resource-intensive recalibration events, the system continuously updates the model using accumulated calibration data, maintaining model validity through ongoing incremental improvements rather than frequent complete recalibrations, thus improving update efficiency.
Data Source
AI summary
The subject of the invention is a method for calibrating a direct neural interface. The calibration is performed by considering a so-called input calibration tensor, formed on the basis of measured electrophysiological signals and so-called output calibration tensor, formed on the basis of measured output signals. The method comprises the application of a least squares multivariate regression implemented by considering a covariance tensor and a cross-covariance tensor which are established on the basis of input and output calibration tensors corresponding to a current calibration period. The method takes into account covariance and cross-covariance tensors established during an earlier calibration period prior to the current calibration period, these tensors being weighted by a forget factor.