AI-POWERED EEG SYSTEM WITH PATHWAY HIERARCHICAL ADAPTIVE REFERENCING FOR LOCALIZED DETECTION, AUTOMATED REPORTING, AND IOMT-ENABLED ADAPTIVE NEUROMODULATION

Smart overview of the Invention

An advanced AI-enabled electroencephalography (EEG) system is introduced, integrating Pathway Hierarchical Adaptive Referencing (PHAR) for precise signal detection, large language models (LLMs) for automated reporting, and Internet of Medical Things (IoMT) for adaptive neuromodulation. This system also supports transcranial electrical stimulation (tES) and functions as an electrical impedance tomography (EIT) system. The PHAR employs a multi-layered multiplexer and adaptive referencing to enhance EEG signal acquisition and spatial resolution. The integration of LLMs facilitates the automated generation of human-readable reports, while IoMT connectivity enables real-time EEG analysis to adjust stimulation parameters, offering a comprehensive tool for brain function modulation.

Technological Advancements

Recent advancements in artificial intelligence (AI), large language models (LLMs), and IoMT technologies address the limitations of conventional EEG systems. AI and machine learning enable sophisticated analysis of neural data, while LLMs enhance interpretability through natural language processing. IoMT facilitates the integration of medical devices into interconnected healthcare ecosystems, promoting real-time data sharing and adaptive treatment paradigms. These technologies collectively enhance EEG-based diagnostics, treatment, and research by providing high-resolution mapping, artifact removal, and adaptive analysis.

Unmet Needs

Despite technological progress, several unmet needs persist in EEG-based applications:

• High-resolution EEG mapping for accurate diagnosis and targeted interventions.
• Effective artifact removal and noise suppression to ensure clear neural activity representation.
• Adaptive EEG analysis tailored to individual brain anatomy and neurophysiology.
• Real-time closed-loop feedback for continuous monitoring and adaptive interventions.
• Integration with AI and LLMs to enhance data interpretation and accessibility.
• Seamless IoMT integration for remote monitoring and personalized treatment protocols.

Core Innovations

The AI-powered EEG system offers unprecedented precision and adaptability through:

• PHAR circuits optimizing electrode clustering for maximal signal quality.
• AI techniques for artifact removal, source localization, and EEG classification.
• LLMs for automated, human-readable EEG report generation.
• IoMT networks for closed-loop adaptive neuromodulation control.

The system's high-density electrode array enables high-resolution spatial sampling, dynamically adjusting configurations based on real-time EEG characteristics and clinical information. AI components employ machine learning models, including CNNs, RNNs, GANs, and GNNs, for noise reduction and precise EEG analysis.

Comprehensive Integration

By integrating GNNs, the system models complex relationships between EEG channels and brain networks, enhancing EEG analysis accuracy. LLMs generate coherent, informative reports, improving clinical utility. The EIT component provides impedance-based imaging for neuromodulation parameter adjustment. IoMT integration supports closed-loop neuromodulation, optimizing therapeutic outcomes by adjusting stimulation parameters in real-time. The system's connectivity with various IoMT devices offers a holistic approach to patient care, integrating with devices like ventilators for comprehensive treatment strategies.