Adaptive EEG Referencing for Localized Signal Detection
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Solution Overview
Problem
Conventional EEG systems suffer from low spatial resolution, noise contamination, lack of adaptability, and inability to provide real-time optimization, limiting precise localization of neural sources and personalized analysis.
Innovation Solution
An AI-powered EEG system with Pathway Hierarchical Adaptive Referencing (PHAR) using a high-density electrode array, multi-layered multiplexer hierarchy, advanced machine learning models, and IoMT integration for adaptive neuromodulation, enabling dynamic electrode clustering, artifact removal, and automated reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional EEG electrode arrays are used, then the system is simple and easy to operate, but spatial resolution is low and localization precision is poor
Solution Approach 1:
The electrode array is segmented into multiple clusters or groups that can be independently configured. The system divides the scalp into multiple regions of interest, with electrodes within each region forming a cluster that can be referenced together, thereby improving spatial resolution while managing complexity through modular organization
Solution Approach 2:
The electrode referencing scheme is made dynamic and adaptive rather than fixed. The system continuously adjusts which electrodes serve as references based on real-time signal quality, artifact levels, and anatomical considerations, allowing optimal spatial resolution to be achieved adaptively without overwhelming complexity
2Adaptability or versatility
If conventional fixed referencing schemes are used, then the system is simple to implement, but adaptability to individual variations and real-time optimization is lost
Solution Approach 1:
The EEG system performs self-calibration and self-optimization by automatically analyzing its own recorded signals to identify artifacts, determine optimal reference electrodes, and adjust clustering configurations without requiring manual intervention or complex external calibration procedures
Solution Approach 2:
The system implements continuous feedback loops where recorded EEG signals are analyzed in real-time to assess signal quality and artifact levels, which then feeds back to dynamically adjust the referencing scheme and electrode clustering to optimize performance for each individual subject
3Measurement precision
If high-density electrode arrays are used, then spatial resolution improves, but noise contamination and artifact susceptibility increase
Solution Approach 1:
The system extracts and removes artifact-contaminated channels or clusters from the active recording configuration. By identifying electrodes that are picking up excessive noise or artifacts and excluding them from the referencing scheme, the system maintains high spatial resolution from the remaining clean electrodes while eliminating the harmful noise components
Data Source
AI summary
The present invention describes an artificial intelligence (AI) enabled electroencephalography (EEG) system that integrates Pathway Hierarchical Adaptive Referencing (PHAR) for localized signal detection, large language models (LLMs) for automated EEG reporting, and Internet of Medical Things (IoMT) connectivity for adaptive neuromodulation control. The system can also deliver transcranial electrical stimulation (tES) pulses and function as an electrical impedance tomography (EIT) system. PHAR employs a multi-layered multiplexer hierarchy and adaptive referencing topologies to optimize EEG signal acquisition and spatial resolution. LLM integration enables automated generation of human-readable EEG reports. IoMT connectivity allows closed-loop neuromodulation, where real-time EEG analysis guides the adjustment of stimulation parameters. The system can deliver tES pulses and perform EIT expands its functionality, allowing for targeted neuromodulation and impedance-based brain imaging. This integrated system revolutionizes EEG-based diagnostics, treatment, and research in neurology and neuroscience, offering a comprehensive and versatile tool for understanding and modulating brain function.