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

TL;DR

This paper introduces a MIMO-based framework for modeling EEG-to-ECoG wireless channels, integrating communication theory with neurophysiology to improve estimation accuracy and facilitate neural interfacing advancements.

Contribution

It presents the first frequency-division MIMO estimation method for brain signals, combining neurophysiology-informed regularization with communication system models.

Findings

Enhanced channel estimation accuracy over traditional methods

Identified a trade-off between frequency resolution and temporal stability

Established a conceptual link between neural signals and multi-antenna communication systems

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.