Adaptive Neurofeedback Threshold Computation via EEG Neuromarkers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current neurofeedback training techniques rely heavily on practitioner skill and judgment for initial selection and in-session adjustment of the reward threshold, leading to variability in treatment efficacy, increased costs due to scarcity of skilled practitioners, and potential annoyance to subjects from intrusive adjustments.
Innovation Solution
An unsupervised adaptive threshold neurofeedback system that automatically adjusts the reward threshold based on neuromarker values from EEG measurements, using a processor to compute an adaptive threshold through a multiplication product of a reward threshold adjustment factor and training protocol values, allowing for real-time adjustments without practitioner intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If practitioner-based threshold selection and adjustment is used, then treatment efficacy can be optimized through expert judgment, but variability in treatment outcomes increases due to practitioner skill differences and unintentional biases
Solution Approach 1:
The system performs self-adjustment of the reward threshold through automated algorithms that analyze EEG data and compute optimal threshold values without practitioner intervention. The processor automatically determines neuromarker values, computes mean values, and adjusts the threshold based on multiplication products of adjustment factors and training protocol values, enabling the system to serve itself rather than relying on external practitioner judgment.
Solution Approach 2:
The patent replaces the mechanical system of practitioner judgment and manual adjustment with an automated computational system. The processor-based algorithm substitutes human cognitive processes with mathematical computations that consistently calculate threshold values based on EEG data, eliminating variability introduced by different practitioners' skills and biases.
2Reliability
If skilled practitioners are used for threshold adjustment, then treatment quality improves, but treatment cost increases due to scarcity of skilled practitioners
Solution Approach 1:
The system performs self-adjustment of the reward threshold through automated algorithms that analyze EEG data and compute optimal threshold values without practitioner intervention. The processor automatically determines neuromarker values, computes mean values, and adjusts the threshold based on multiplication products of adjustment factors and training protocol values, enabling the system to serve itself rather than relying on external practitioner judgment.
Solution Approach 2:
The patent creates a computational model that copies and formalizes the decision-making process of skilled practitioners into an automated algorithm. By encoding threshold adjustment logic into software that processes EEG data through defined mathematical operations, the system replicates expert judgment capabilities without requiring the physical presence of skilled practitioners, thereby reducing treatment costs while maintaining quality.
3Adaptability or versatility
If in-session threshold adjustment is performed manually, then treatment adaptability improves, but session continuity is disrupted and subjects experience annoyance
Solution Approach 1:
The system implements dynamic threshold adjustment where the reward threshold is continuously adapted during the training session based on real-time EEG data analysis. The processor monitors neuromarker values and automatically updates the threshold through computed multiplication products, allowing the system to adapt to changing brainwave patterns without manual intervention or session disruption.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously monitors EEG data, compares actual brainwave characteristics against target patterns, and automatically adjusts the reward threshold based on the detected deviation. This closed-loop feedback system maintains optimal threshold levels dynamically while operating transparently without interrupting the training session or requiring subject awareness of the adjustment process.
4Extent of automation
If automated threshold adjustment is implemented, then practitioner dependency decreases and costs reduce, but system complexity increases
Solution Approach 1:
The patent segments the threshold adjustment process into distinct computational modules: EEG data acquisition, neuromarker value determination, mean value computation, adjustment factor application through multiplication, and threshold output generation. This modular segmentation organizes the automated process into manageable functional blocks that can be implemented and maintained more easily despite the overall automation complexity.
Data Source
AI summary
Examples include receiving and storing samples of target frequency bands filtered from EEG measurement of a subject's brain waves in an NFB training session. In an example, upon storing a time window of the samples, unsupervised adaptive adjusting an NFB reward threshold is automatic. The adjusting includes, in examples, determining neuromarker values in the time window, which indicate peak values of the target frequency bands over the time window. The adjusting computes the mean value of the neuromarker values and, utilizing same, automatically proceeds to unsupervised computing an adaptive adjusted reward threshold. The unsupervised computing, in examples, includes a multiplication product of a reward threshold adjustment factor, a training protocol value, and the computed mean value of the neuromarker values. Examples proceed to communicating the adaptive adjusted reward threshold to a controller for threshold based feedback reward to the NBF subject.