Neural Activation Assessment via Bioelectrical Signal Variance
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Solution Overview
Problem
Current methods for assessing neural activation in the brain are inadequate, as they struggle to accurately determine the relative level of neural activity, especially in conditions like neurological injuries and diseases, where normal patterns of neuronal activity are disrupted, making it difficult to effectively administer therapies that target specific brain areas.
Innovation Solution
The method involves sensing bioelectrical signals, calculating variance values over time, and assessing the relative level of neural activation in brain areas using control circuitry, which estimates functional synaptic volume and compares variance values to determine changes in neural activity, allowing for the titration of therapies and tracking of brain conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods are used to assess neural activation, then the assessment process is simple, but the measurement precision of neural activation levels is inadequate
Solution Approach 1:
The patent transforms the assessment from direct neural activation measurement to indirect measurement through variance analysis of bioelectrical signals. By changing the parameter being measured from activation level directly to signal variance over time, the system achieves more precise neural activation assessment while working within the constraints of available sensing technology.
Solution Approach 2:
The patent introduces variance calculation as an intermediary step between raw bioelectrical signal sensing and neural activation assessment. This intermediary transformation allows the system to derive meaningful neural activation information from complex bioelectrical signals, improving measurement precision without requiring direct neural measurement capabilities.
2Measurement precision
If variance values are calculated over multiple time intervals to improve neural activation assessment, then the measurement precision increases, but the loss of time for data processing increases
Solution Approach 1:
The patent performs preliminary calculations of variance values across multiple time intervals in advance, storing these pre-computed values for later comparison. This preliminary action allows the system to have accurate neural activation assessments ready when needed, reducing the time loss during actual assessment by avoiding real-time recomputation.
3Reliability
If bioelectrical signals are sensed continuously to track neural activation changes, then the reliability of neural activation tracking improves, but the use of energy increases
Solution Approach 1:
The patent implements periodic sampling of bioelectrical signals at discrete time intervals rather than continuous sensing. This periodic approach maintains reliable neural activation tracking by capturing sufficient temporal information while significantly reducing energy consumption compared to continuous monitoring, as the system only processes data at specific intervals.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables effective assessment and management of neural activation levels, allowing for the adjustment of therapies based on real-time data, thereby improving treatment outcomes for neurological conditions by accurately measuring changes in brain activity over time.
Implementation Method 1
sensing one or more bioelectrical signals from one or more electrodes in contact with or proximate a brain
Data Source
AI summary
Various embodiments concern sensing a LFP signal from one or more electrodes, measuring the amplitude of the signals over a period of time, and calculating a plurality of variance values from the amplitude, wherein each of the variance values correspond to the variance of the amplitude for a different interval of time of the period of time with respect to the other variance values. Such embodiments may further include assessing the relative level of neural activation of an area of the brain based on the variance values, wherein the area of the brain is assessed to have a relatively higher level of neural activation when the variance is relatively higher and the area of the brain is assessed to have a relatively lower level of neural activation when the variance is relatively lower.