Closed-Loop Brain Control System for Neural Stimulation
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Solution Overview
Problem
Traditional neuronal signal modeling mechanisms have limitations, such as only measuring simple behavior signatures and failing to accurately model complex neural systems, leading to inefficient and potentially erroneous open-loop stimulation techniques that can result in over- or under-stimulation.
Innovation Solution
A closed-loop system that uses an interface to obtain brain measurements, generate brain state parameters, and create adaptive models to estimate desired brain states, allowing for precise control signals to be generated and adjusted dynamically, incorporating machine learning and artificial intelligence for enhanced therapeutic and cognitive modulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional open-loop stimulation techniques are used, then the system is simple to operate, but the stimulation precision and effectiveness deteriorate due to over- or under-stimulation
Solution Approach 1:
The patent implements a closed-loop control system where neural activity measurements are continuously fed back to adjust stimulation parameters in real-time. The controller receives measurements from the brain interface, compares current brain state to desired state, and dynamically adjusts stimulation signals to achieve precise control while avoiding over- or under-stimulation.
Solution Approach 2:
The system uses the brain's own neural activity measurements to automatically regulate stimulation delivery. The measured neural activity serves as both the control input and the regulated output, enabling the system to self-adjust without external intervention and achieve precise therapeutic effects.
2Measurement precision
If traditional neuronal signal modeling is used, then the measurement process is simple, but the modeling accuracy deteriorates due to inability to capture complex neural systems
Solution Approach 1:
The patent segments the complex neural system into multiple measurable components including local field potentials, spike rates, and oscillatory power in different frequency bands. Each component is measured and modeled separately, then integrated to form a comprehensive brain state representation that captures complex neural dynamics.
Solution Approach 2:
The system transforms raw neural signals into multiple derived parameters including power spectral density, coherence, and information theory metrics. These parameter transformations enable accurate characterization of complex brain states while providing controllable inputs for the closed-loop system.
3Reliability
If traditional open-loop stimulation is used, then the treatment protocol is simple to implement, but the therapeutic effectiveness deteriorates due to inability to adapt to individual brain states
Solution Approach 1:
The patent implements dynamic adaptation where stimulation parameters are continuously adjusted based on real-time neural activity measurements. The controller dynamically modifies stimulation amplitude, frequency, and duration to match the patient's current brain state, ensuring optimal therapeutic effectiveness while adapting to changing neural conditions.
Solution Approach 2:
The closed-loop system continuously measures neural activity, compares it to desired therapeutic states, and adjusts stimulation accordingly. This feedback mechanism ensures reliable therapeutic outcomes by adapting treatment to individual patient needs and real-time brain state variations.
Data Source
AI summary
Provided are systems, methods, and devices for closed loop control associated with brain activity. Systems may include an interface configured to obtain a plurality of measurements from a brain of a user, and a first processing device including one or more processors configured to generate a plurality of brain state parameters characterizing one or more features of at least one brain state of the user. The systems may also include, a second processing device including one or more processors configured to generate at least one model of the brain of the user based, at least in part, on the plurality of brain state parameters and the plurality of measurements. The systems may further include a controller including one or more processors configured to generate a control signal based on the plurality of brain state parameters and the at least one model.