Closed-loop neurostimulation latency compensation
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Solution Overview
Problem
Current neuromodulation systems fail to provide adaptive and automatic neurostimulation interventions based on real-time monitoring of neurophysiological signals, lacking individualization and precision in timing and stimulation adaptation.
Innovation Solution
A system that continuously monitors neurophysiological signals, measures latencies, and adjusts neurostimulation protocols in real-time to administer targeted interventions during specific temporal regions of interest, classifying and replicating signal properties for personalized stimulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple threshold crossing is used to trigger intervention, then device complexity is reduced, but measurement precision and adaptability of neurostimulation to individual brain states deteriorates
Solution Approach 1:
The system transitions from static threshold-based triggering to dynamic, adaptive classification of neurophysiological signals. The machine learning model continuously analyzes brain state characteristics and adjusts stimulation parameters accordingly, enabling the system to adapt to individual neural patterns while maintaining manageable complexity through automated decision-making
Solution Approach 2:
The invention changes the triggering parameters from fixed thresholds to dynamically adjusted stimulation parameters based on classified brain states. The system modifies stimulation characteristics (timing, intensity, duration) according to the specific neurophysiological state detected, thereby improving precision without proportionally increasing system complexity
2Adaptability or versatility
If state-based triggering of static audio stimuli is used, then adaptability to brain states is improved, but timing precision and latency of neurostimulation intervention deteriorates
Solution Approach 1:
The system performs preliminary classification of brain states and prediction of optimal stimulation timing before actually delivering the intervention. By anticipating the optimal moment for stimulation based on ongoing neurophysiological patterns, the system reduces latency while maintaining state-based adaptability
Solution Approach 2:
The invention implements closed-loop feedback where the system continuously monitors neurophysiological responses and adjusts stimulation timing accordingly. This feedback mechanism allows the system to optimize the timing of interventions in real-time, reducing latency while preserving adaptability to individual brain states
3Ease of operation
If predetermined library of stimulation protocols is used, then ease of operation is improved, but adaptability to individual neurophysiological properties deteriorates
Solution Approach 1:
The system performs self-service by automatically classifying brain states and selecting appropriate stimulation protocols without requiring manual configuration. The machine learning model autonomously adapts stimulation parameters to individual neurophysiological properties, maintaining ease of operation while achieving high levels of personalization
Solution Approach 2:
The invention creates a universal system that can handle multiple brain states and individual variations through a single integrated platform. The machine learning model serves multiple functions (classification, prediction, parameter adjustment) within one system, maintaining operational simplicity while providing tailored stimulation for each user
4Productivity
If continuous method of generating stimulation predictions is used, then productivity of neurostimulation delivery is improved, but measurement precision of phase and frequency prediction deteriorates
Solution Approach 1:
The system uses dynamic prediction methods that continuously update phase and frequency estimates based on ongoing neurophysiological signals. The machine learning model adapts its predictions in real-time according to changing brain states, maintaining both high productivity through continuous processing and precision through adaptive algorithms
Data Source
AI summary
Described is a system for synchronization of neurostimulation interventions. The system continuously monitors incoming neurophysiological signals. Latencies present in the monitoring of the incoming neurophysiological signals are measured. Based on the measured latencies, the timing of targeted neurostimulation interventions is determined, resulting in a neurostimulation intervention protocol. The neurostimulation intervention protocol is adjusted in real time for administration of neurostimulation during temporal regions of interest. The system then triggers administration of the neurostimulation during the temporal regions of interest.