ANNI Neural Signal Stabilization for BCI Recalibration

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Solution Overview

Problem

Brain-computer interfaces (BCIs) require frequent recalibration due to signal degradation from physical or biological changes, leading to reduced functionality and inconvenience for patients and clinicians.

Innovation Solution

A method using adversarial networks for neural interfaces (ANNI) that trains multiple neural translation models to stabilize disrupted neural signals by translating them back to their original state, employing autoencoders, discriminators, and penalty drift models to maintain signal class information and prevent class swapping, allowing for unsupervised and automated signal stabilization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual recalibration is performed frequently to maintain BCI functionality, then decoder accuracy is maintained, but patient convenience deteriorates and time loss increases

Engineering Contradiction:

Improvedecoder accuracyVSAvoidrecalibration time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs automatic recalibration using unsupervised learning algorithms that continuously adapt to signal changes without requiring patient participation or clinician intervention. The algorithm autonomously detects signal disruptions and recalibrates the decoder, making the system self-maintaining and eliminating the need for manual recalibration sessions.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system prepares multiple pre-trained translation models in advance that can handle different types of signal disruptions. When signal degradation is detected, the system can immediately switch to a pre-prepared model rather than performing recalibration from scratch, reducing the time and effort required for maintenance.

Inventive Principle:

Principle #10Preliminary action

2Reliability

If manual recalibration is performed frequently to maintain BCI functionality, then decoder accuracy is maintained, but patient convenience deteriorates

Engineering Contradiction:

Improvedecoder accuracyVSAvoidpatient convenience

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

Inventive Principle:

Principle #25Self-service

3Device complexity

If neural signals are used without stabilization, then device complexity is reduced, but signal reliability deteriorates due to physical and biological changes

Engineering Contradiction:

Improvesystem simplicityVSAvoidsignal stability

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

The system replaces manual recalibration procedures with an automated machine learning algorithm that uses unsupervised learning to detect and correct signal disruptions. The algorithm substitutes physical recalibration actions with computational processing, maintaining signal reliability while reducing the need for manual intervention and simplifying the overall system operation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system changes the operational parameters of the BCI by introducing adaptive translation models that dynamically adjust to signal characteristics. Multiple pre-trained models with different parameter configurations are maintained, allowing the system to adapt to various signal disruption scenarios without requiring fundamental changes to the hardware or basic system architecture.

Inventive Principle:

Principle #35Parameter changes

4Reliability

If multiple translation models are trained and maintained to handle signal disruptions, then signal stability is improved, but device complexity increases

Engineering Contradiction:

Improvesignal stabilityVSAvoidmodel management complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system prepares multiple pre-trained translation models in advance that can handle different types of signal disruptions. By pre-training these models during the initial setup phase, the system eliminates the need for complex real-time model training and selection, reducing operational complexity while maintaining the ability to handle various signal conditions.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates copies of the translation model in different pre-trained states, each optimized for specific signal conditions. These model copies are stored and can be rapidly switched between based on the current signal characteristics, avoiding the need for complex real-time adaptation while maintaining signal stability across different conditions.

Inventive Principle:

Principle #26Copying

Data Source

PatentUS20220383107A1Method for neural signals stabilization

Publication Date: 2022.12.01 TELEDYNE SCIENTIFIC & IMAGING LLC

AI summary

A method for stabilizing disrupted neural signals received by a brain-computer interface (BCI), where a translation model is trained on a clean and disrupted dataset and is used to translate a disrupted signal to a clean signal. The clean dataset is based on the data that is received the same day the BCI is calibrated and the disrupted dataset is based on data received the same day that the model is trained. Based on the variation in daily signal disruption, the training model is retrained each day and a new translation model is applied to a disrupted dataset.