EEG Digital Twin Modeling for Real-Time Brain Wave Imaging
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Solution Overview
Problem
Existing EEG systems lack the capability to process high spatial resolution data, which is essential for true brain wave imaging, and current EEG devices do not provide real-time brain imaging, which is essential for clinical diagnosis and treatment of brain disorders.
Innovation Solution
The use of adaptive digital twin (ADT) technology that employs advanced EEG analysis algorithms and software with high temporal and spatial resolution, providing real-time brain wave imaging using a sparse sensor network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional EEG systems are used, then the system complexity is low, but the spatial resolution and real-time imaging capability are insufficient
Solution Approach 1:
The patent creates a computational model (digital twin) of the brain's electrical activity that copies and processes EEG data to generate high-resolution spatial maps. This virtual representation allows sophisticated analysis without requiring equally sophisticated physical sensor arrays, resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
The patent introduces an intermediary computational processing layer between the simple EEG sensors and the final brain imaging output. This intermediate processing stage transforms raw sensor data into high-resolution spatial representations through algorithmic manipulation, achieving high precision without direct hardware complexity.
2Productivity
If traditional EEG systems are used, then the device cost is low, but real-time brain imaging capability is not provided
Solution Approach 1:
The system creates a computational copy of brain activity patterns from inexpensive EEG sensors, enabling real-time imaging through software processing rather than expensive hardware. The digital twin model processes sensor data in real-time to generate instantaneous brain state visualizations, achieving high productivity without proportionally high cost.
Solution Approach 2:
The patent replaces complex mechanical/imaging hardware systems with computational algorithms that process EEG signals in real-time. Instead of using expensive physical imaging devices, the system uses software-based signal processing and visualization to achieve real-time brain imaging capability, reducing cost while maintaining productivity.
3Measurement precision
If high spatial resolution EEG analysis is performed, then the measurement precision improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary computational setup and model initialization before actual brain imaging is required. The digital twin model is pre-configured with appropriate parameters and processing algorithms, enabling rapid real-time analysis without time-consuming computation during actual use. This preliminary preparation eliminates processing time delays while maintaining high spatial resolution.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for characterizing brain wave patterns are disclosed. In one aspect, a method includes the actions of receiving, from first electroencephalogram (EEG) sensors that are physically detecting activity of a brain of a first patient, first EEG sensor outputs. The actions further include, based on the first EEG sensor outputs, determining relationships between the first EEG sensor outputs and first cognitive states of the first patient. The actions further include receiving, from second EEG sensors that are physically detecting activity of a brain of a second patient, second EEG sensor outputs. The actions further include, based on the relationships between the first EEG sensor outputs and the first cognitive states of the first patient and based on the second EEG sensor outputs, determining a cognitive state of the second patient.