Sleep State Modeling With Multimodal Brain Signals and Closed-Loop Stimulation
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Solution Overview
Problem
Traditional techniques for measuring and stimulating brain electrical activity are limited in their ability to determine suitable stimulation parameters, often resulting in over-stimulation or under-stimulation due to insufficient sensitivity and localization, and lack the capability to accurately model complex neural systems.
Innovation Solution
The use of intracranial modalities, such as electrocorticography (ECoG) and intracranial electroencephalography (iEEG), combined with surface modalities, to enhance brain state and functional models, allowing for the generation of accurate stimulation parameters and control signals to transition between sleep stages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional measuring modalities are used to measure brain electrical activity, then the measurement process is simple and non-invasive, but the sensitivity and localization ability are insufficient
Solution Approach 1:
The system segments brain measurement into multiple modalities (intracranial ECoG electrodes for high precision local measurement, surface EEG for broader coverage, fMRI for structural context). Each modality targets specific brain regions or functions, allowing the system to achieve high sensitivity and localization by combining specialized measurement approaches rather than relying on a single general-purpose method.
Solution Approach 2:
The system transitions from traditional single-modality measurement to multi-dimensional measurement by integrating intracranial electrodes (providing millimeter-scale spatial resolution), surface EEG (providing temporal dynamics), and fMRI (providing metabolic and structural information). This multi-dimensional approach enables comprehensive brain state modeling that captures neural activity across multiple spatial and functional scales simultaneously.
2Manufacturing precision
If traditional stimulation techniques are used, then the stimulation process is simple to implement, but the ability to determine suitable stimulation parameters is limited resulting in over-stimulation or under-stimulation
Solution Approach 1:
The system implements closed-loop feedback control where brain state models generated from multi-modal measurements continuously inform stimulation parameter adjustments. The controller monitors neural activity patterns, compares them against desired sleep stage transitions, and dynamically modifies stimulation parameters (intensity, frequency, duration) to achieve precise control over sleep stage progression while preventing over-stimulation or under-stimulation.
Solution Approach 2:
The system transitions from static, pre-programmed stimulation protocols to dynamic, adaptive stimulation control. Brain state models are continuously updated based on real-time multi-modal measurements, allowing stimulation parameters to adapt dynamically to the user's current neural state. This enables precise timing and dosing of stimulation to facilitate optimal transitions between sleep stages.
3Measurement precision
If multi-modal measurements are integrated to enhance brain state modeling, then the accuracy of sleep stage identification improves, but the system complexity increases
Solution Approach 1:
The system merges multiple measurement modalities (intracranial ECoG, surface EEG, fMRI) into a unified brain state model. By combining the high spatial precision of intracranial electrodes with the temporal resolution of surface EEG and the metabolic information from fMRI, the system achieves comprehensive sleep stage identification that leverages the complementary strengths of each modality while managing integration complexity through coordinated data processing.
Solution Approach 2:
The brain state model serves as a universal framework that processes and integrates data from multiple measurement modalities. This multi-functional model can analyze neural activity patterns across different spatial and temporal scales, identify various sleep stages, and guide stimulation protocols for different therapeutic objectives, thereby managing system complexity through a centralized analytical platform.
Data Source
AI summary
Provided are systems, methods, and devices for measurement, identification, and generation of sleep state models. Systems include a plurality of electrodes configured to be coupled to a brain of a user and configured to obtain a plurality of measurements from the brain of the user, and an interface configured to obtain the plurality of measurements from the plurality of electrodes. Systems include a processing device comprising one or more processors configured to generate a sleep state model associated with the user, the sleep state model identifying characteristics of a plurality of sleep stages, and further identifying characteristics of transitions between the plurality of sleep stages. Systems include a controller comprising one or more processors configured to generate a control signal based on the sleep state model and the plurality of measurements.