Bridging BCI and Communications: A MIMO Framework for EEG-to-ECoG Wireless Channel Modeling

2025-05-16

AI Summary

This work addresses the challenge of modeling the wireless propagation channel for intracranial electrocorticographic (ECoG) neural signals through the skull to extracranial EEG electrodes. We propose the first MIMO channel modeling framework specifically designed for wireless neural signal transmission. Innovatively integrating multi-antenna communication theory into neural interfaces, we develop a neurophysiologically guided, frequency-division-based MIMO estimation method that jointly leverages prior regularization and dual-modality (EEG/ECoG) data from synchronized macaque experiments. Our approach significantly improves channel estimation accuracy and, for the first time, uncovers an intrinsic trade-off between frequency resolution and temporal stability in neural signal propagation. The resulting computationally tractable and experimentally verifiable channel model establishes a foundational basis for the deep integration of brain–computer interfaces (BCIs) with 6G-enabled neural communication systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Mixture of Experts (MoE)
• Multiagent Systems: Agent Communication

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Web Mining and Content Analysis: Web data generation and simulation
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

As a method to connect human brain and external devices, Brain-computer interfaces (BCIs) are receiving extensive research attention. Recently, the integration of communication theory with BCI has emerged as a popular trend, offering potential to enhance system performance and shape next-generation communications. A key challenge in this field is modeling the brain wireless communication channel between intracranial electrocorticography (ECoG) emitting neurons and extracranial electroencephalography (EEG) receiving electrodes. However, the complex physiology of brain challenges the application of traditional channel modeling methods, leaving relevant research in its infancy. To address this gap, we propose a frequency-division multiple-input multiple-output (MIMO) estimation framework leveraging simultaneous macaque EEG and ECoG recordings, while employing neurophysiology-informed regularization to suppress noise interference. This approach reveals profound similarities between neural signal propagation and multi-antenna communication systems. Experimental results show improved estimation accuracy over conventional methods while highlighting a trade-off between frequency resolution and temporal stability determined by signal duration. This work establish a conceptual bridge between neural interfacing and communication theory, accelerating synergistic developments in both fields.

Problem

• Modeling wireless channel between ECoG neurons and EEG electrodes
• Applying MIMO framework to neural signal propagation
• Improving estimation accuracy with neurophysiology-informed methods

Innovation

• MIMO framework for EEG-to-ECoG channel modeling
• Neurophysiology-informed regularization reduces noise
• Frequency-division enhances neural signal estimation