Nonlinear Dynamical Modeling of Human Intracranial Brain Activity with Flexible Inference

TL;DR

This paper introduces DFINE, a deep learning framework that extends nonlinear dynamical modeling to multisite human intracranial EEG, improving forecasting accuracy and robustness in the presence of missing data for brain-computer interfaces.

Contribution

The paper extends the DFINE framework to multisite human iEEG, demonstrating its superior forecasting and missing data handling over linear models and matching RNNs.

Findings

DFINE outperforms linear models in neural forecasting.

DFINE matches or exceeds GRU accuracy in predicting neural signals.

DFINE is more robust with missing observations, especially in high gamma bands.

Abstract

Dynamical modeling of multisite human intracranial neural recordings is essential for developing neurotechnologies such as brain-computer interfaces (BCIs). Linear dynamical models are widely used for this purpose due to their interpretability and their suitability for BCIs. In particular, these models enable flexible real-time inference, even in the presence of missing neural samples, which often occur in wireless BCIs. However, neural activity can exhibit nonlinear structure that is not captured by linear models. Furthermore, while recurrent neural network models can capture nonlinearity, their inference does not directly address handling missing observations. To address this gap, recent work introduced DFINE, a deep learning framework that integrates neural networks with linear state-space models to capture nonlinearities while enabling flexible inference. However, DFINE was developed for intracortical recordings that measure localized neuronal populations. Here we extend DFINE to modeling of multisite human intracranial electroencephalography (iEEG) recordings. We find that DFINE significantly outperforms linear state-space models (LSSMs) in forecasting future neural activity. Furthermore, DFINE matches or exceeds the accuracy of a gated recurrent unit (GRU) model in neural forecasting, indicating that a linear dynamical backbone, when paired and jointly trained with nonlinear neural networks, can effectively describe the dynamics of iEEG signals while also enabling flexible inference. Additionally, DFINE handles missing observations more robustly than the baselines, demonstrating its flexible inference and utility for BCIs. Finally, DFINE's advantage over LSSM is more pronounced in high gamma spectral bands. Taken together, these findings highlight DFINE as a strong and flexible framework for modeling human iEEG dynamics, with potential applications in next-generation BCIs.