Neurofeedback Threshold Adaptation for Low-Powered Devices
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Solution Overview
Problem
Conventional neurofeedback systems face challenges in dynamically adjusting threshold values, leading to inconsistent training sessions due to fixed activity rates and susceptibility to noise artifacts, which can hinder effective brain activity modification.
Innovation Solution
A computer-implemented system that captures bio-signals to measure baseline brain activity and computes adaptive threshold values using an activity model, incorporating mean and standard deviation calculations, artifact detection, and recalibration mechanisms to provide real-time feedback and optimize neurofeedback sessions on low-powered devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated thresholding is applied using fixed activity rates, then the system can operate without manual intervention, but the feedback becomes inconsistent and susceptible to noise artifacts
Solution Approach 1:
The patent implements dynamic threshold adjustment by continuously adapting the threshold based on rolling statistical calculations (mean and standard deviation) of biomarker values. Instead of using fixed thresholds, the system recalculates thresholds in real-time windows, allowing the threshold to adapt to changing baseline activity levels and reduce susceptibility to noise artifacts while maintaining automated operation.
Solution Approach 2:
The system incorporates feedback mechanisms where the activity rate calculated from threshold comparisons is used to adjust future threshold values. The activity rate itself becomes a feedback signal that informs subsequent threshold adjustments, creating a closed-loop system that improves reliability by adapting to actual user performance rather than relying on predetermined fixed thresholds.
2Adaptability or versatility
If the threshold is set too high, then the training session becomes more challenging, but the user may become discouraged and unable to achieve desired brain activity changes
Solution Approach 1:
The threshold dynamically adapts to user performance through continuous recalibration. When users consistently achieve above-threshold activity, the threshold automatically increases to maintain challenge appropriateness. This dynamic adjustment ensures the training remains challenging without becoming excessively difficult, preventing user discouragement while maintaining adaptability to individual user capabilities.
Solution Approach 2:
The system changes the threshold parameter based on observed user performance and activity patterns. By monitoring whether users consistently meet or exceed the threshold, the system adjusts the threshold value to optimize the balance between challenge and achievability, ensuring users remain engaged without feeling overwhelmed.
3Ease of operation
If the threshold is set too low, then the user can easily achieve feedback, but the training session becomes boring and lacks effectiveness
Solution Approach 1:
The dynamic threshold adjustment mechanism prevents the threshold from remaining too low by automatically increasing it when users consistently achieve above-threshold activity. This ensures the training maintains effectiveness by progressively increasing challenge levels, preventing boredom while still allowing users to experience success during the adaptation period.
Solution Approach 2:
The system actively changes the threshold parameter in response to user performance patterns. When users easily and consistently exceed the threshold, the system increases the threshold value to restore appropriate challenge levels, thereby maintaining training effectiveness and preventing monotony.
4Adaptability or versatility
If manual thresholding is performed by specialists, then the threshold adjustment can be tailored to individual users, but the process is time-consuming and limited by availability of trained personnel
Solution Approach 1:
The system performs self-service by automatically calculating and adjusting thresholds based on user-specific biomarker data without requiring specialist intervention. The automated system collects baseline data, calculates rolling statistics, and adapts thresholds independently, eliminating the time-consuming manual setup while maintaining personalized adaptation to individual user characteristics.
Solution Approach 2:
The patent replaces the mechanical process of manual specialist threshold setting with an automated computational system. Instead of relying on human specialists to manually adjust thresholds, the system uses algorithmic calculations based on rolling statistical measures of user biomarker data, dramatically reducing setup time while maintaining or improving personalization.
5Device complexity
If fixed activity rates are used in automated thresholding, then the system is simpler to implement, but it cannot account for signal quality and noise artifacts
Solution Approach 1:
The system uses dynamic window-based statistical calculations that adapt to signal quality in real-time. By calculating rolling mean and standard deviation within moving time windows and using these to dynamically adjust thresholds, the system becomes less susceptible to noise artifacts while maintaining relatively simple implementation through standard statistical operations.
Data Source
AI summary
A method for generating a neurofeedback signal for neurofeedback session on low powered devices is disclosed. The method includes (a) capturing one or more bio-signals from one or more electronic devices, comprising one or more digital biomarkers; (b) measuring a baseline activity associated with the captured one or more digital biomarkers during a resting state of the brain; (c) measuring an activity rate based on the one or more digital biomarkers; (d) computing a threshold value at a plurality of time series in the neurofeedback session based on at least one of: the measured activity rate and the one or more digital biomarkers captured during the baseline activity of the brain using an activity model; and (e) outputting of the neurofeedback signal corresponding to the neurofeedback session to the user based on the computed threshold value.