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

TL;DR

This work treats the brain-to-outside-system wireless link as a frequency-division MIMO channel, applying a STARE (Spatial-Temporal Adaptive Regularized Estimation) framework to incorporate neurophysiological priors via spatial smoothness and temporal continuity. Using synchronized macaque ECoG–EEG data, the authors formulate a per-frequency channel model $\mathbf{Y}_k^{(f)} = \mathbf{H}_k^{(f)} \mathbf{X}_k^{(f)} + \mathbf{N}$ and solve for $\mathbf{H}_k^{(f)}$ with ADMM, incorporating a graph-Laplacian regularizer and temporal coupling across frames. Empirical results show STARE reduces mean-squared-error against LS and MMSE by about $15\%$, and uncover a trade-off between symbol length $L$ and the resulting frequency resolution $\Delta f = F_s / L$, yielding an optimal $L_{\text{opt}}$ around $3.3\times 10^4$ samples. This establishes a conceptual bridge between neural interfacing and wireless communication theory and provides guidance for designing brain-channel-aware modulation and decoding in future BCI-enabled networks.

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.

Figures (4)

• Figure 1: The conceptual diagram of brain channel.
• Figure 2: The illustration of FD-MIMO.
• Figure 3: The relationship between the symbol length and the $\text{MSE}_{Avg}$.
• Figure 4: The $\text{MSE}_{Avg}$ at the corresponding symbol length for three methods: STARE, LS, and MMSE. At the far end of the axis, the average MSE across all symbol lengths is presented: 5.44 for STARE, compared to 6.44 for LS and 6.42 for MMSE.