Phase-Dependent Brain Neuromodulation for Energy-Efficient Stimulation
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Solution Overview
Problem
Existing brain stimulation techniques using constant electrical pulses are inefficient, leading to wasted energy resources and poor clinical outcomes due to higher thresholds for stimulation-associated side effects, hindering the miniaturization and effectiveness of brain stimulation devices.
Innovation Solution
Implementing phase-dependent neuromodulation that measures and predicts brain activity to dynamically adjust stimulus pulses based on frequency and phase, reducing energy utilization and improving clinical outcomes by modulating cross-frequency coupling in cortical structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If constant electrical stimulation pulses are applied to the brain, then brain function can be altered, but energy resources are wasted and device miniaturization is hindered
Solution Approach 1:
The patent implements dynamic brain stimulation by continuously monitoring brain activity signals and adjusting stimulation pulse parameters (frequency, phase, amplitude) in real-time based on detected neural oscillations. This replaces constant static stimulation with adaptive dynamic stimulation that responds to ongoing brain states, thereby improving therapeutic effectiveness while reducing energy waste from unnecessary stimulation.
Solution Approach 2:
The system employs closed-loop feedback by detecting brain activity signals, analyzing neural oscillation characteristics, and using this information to modulate subsequent stimulation pulses. The feedback mechanism allows the device to adjust stimulation based on actual brain responses, ensuring energy is applied only when and where needed for therapeutic effect, thus reducing overall energy consumption.
2Reliability
If constant electrical stimulation pulses are applied to the brain, then brain function can be altered, but clinical outcomes are poor due to higher thresholds for stimulation-associated side effects
Solution Approach 1:
By dynamically adjusting stimulation parameters based on real-time brain activity detection, the system applies stimulation at optimal phases and frequencies that are more effective at lower intensities. This dynamic adaptation allows therapeutic effects to be achieved below the thresholds that would cause adverse side effects, improving the safety margin.
Solution Approach 2:
The patent changes multiple stimulation parameters including frequency, phase, and amplitude based on detected brain oscillations. By modulating these parameters dynamically rather than using fixed constant pulses, the system can target specific neural circuits more precisely with lower energy, avoiding the activation of adjacent structures that would produce side effects.
3Reliability
If constant electrical stimulation pulses are applied to the brain, then brain function can be altered, but device miniaturization is hindered
Solution Approach 1:
The system uses dynamic stimulation protocols that require shorter stimulation durations due to phase-dependent efficacy. By applying stimulation only during specific phases of neural oscillations when the target neurons are most responsive, the system achieves therapeutic effects with reduced total stimulation time and energy, allowing for smaller battery and power management components.
Solution Approach 2:
The system maintains continuous monitoring of brain activity and continuously adjusts stimulation parameters, ensuring that every stimulation pulse is therapeutically effective. This continuous adaptive approach reduces the total number of pulses needed compared to constant stimulation, thereby reducing energy requirements and enabling smaller device form factors.
4Use of energy by moving object
If phase-dependent neuromodulation is implemented with dynamic adjustment of stimulus pulses, then energy consumption is reduced, but measurement and prediction of brain activity is required
Solution Approach 1:
The patent replaces complex computational prediction models with simplified detection algorithms that identify characteristic patterns in brain activity signals. By focusing on detecting specific oscillation frequencies and phases rather than predicting complex neural dynamics, the system reduces computational burden while still enabling phase-dependent stimulation timing.
Data Source
AI summary
A device may receive, from one or more electrodes, information identifying brain activity for a first time period. The device may predict, based on the information identifying the brain activity for the first time period, predicted brain activity for a second time period that is to occur after the first time period. The device may determine, based on the predicted brain activity for the second time period, a brain stimulus for the second time period, wherein the brain stimulus is associated with a frequency and a phase determined based on the predicted brain activity for the second time period. The device may cause the brain stimulus to be applied in accordance with the frequency and the phase during the second time period.