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Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flow

Dongyi He, Bin Jiang, Kecheng Feng, Luyin Zhang, Ling Liu, Yuxuan Li, Yun Zhao, He Yan

TL;DR

NeuroFlowNet is a novel cross-modal generative framework whose core contribution lies in the first-ever reconstruction of iEEG signals from the entire deep temporal lobe region using sEEG signals, explicitly capturing the randomness of brain signals and fundamentally avoiding the pattern collapse issues common in existing generative models.

Abstract

Although obtaining deep brain activity from non-invasive scalp electroencephalography (sEEG) is crucial for neuroscience and clinical diagnosis, directly generating high-fidelity intracranial electroencephalography (iEEG) signals remains a largely unexplored field, limiting our understanding of deep brain dynamics. Current research primarily focuses on traditional signal processing or source localization methods, which struggle to capture the complex waveforms and random characteristics of iEEG. To address this critical challenge, this paper introduces NeuroFlowNet, a novel cross-modal generative framework whose core contribution lies in the first-ever reconstruction of iEEG signals from the entire deep temporal lobe region using sEEG signals. NeuroFlowNet is built on Conditional Normalizing Flow (CNF), which directly models complex conditional probability distributions through reversible transformations, thereby explicitly capturing the randomness of brain signals and fundamentally avoiding the pattern collapse issues common in existing generative models. Additionally, the model integrates a multi-scale architecture and self-attention mechanisms to robustly capture fine-grained temporal details and long-range dependencies. Validation results on a publicly available synchronized sEEG-iEEG dataset demonstrate NeuroFlowNet's effectiveness in terms of temporal waveform fidelity, spectral feature reproduction, and functional connectivity restoration. This study establishes a more reliable and scalable new paradigm for non-invasive analysis of deep brain dynamics. The code of this study is available in https://github.com/hdy6438/NeuroFlowNet

Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flow

TL;DR

NeuroFlowNet is a novel cross-modal generative framework whose core contribution lies in the first-ever reconstruction of iEEG signals from the entire deep temporal lobe region using sEEG signals, explicitly capturing the randomness of brain signals and fundamentally avoiding the pattern collapse issues common in existing generative models.

Abstract

Although obtaining deep brain activity from non-invasive scalp electroencephalography (sEEG) is crucial for neuroscience and clinical diagnosis, directly generating high-fidelity intracranial electroencephalography (iEEG) signals remains a largely unexplored field, limiting our understanding of deep brain dynamics. Current research primarily focuses on traditional signal processing or source localization methods, which struggle to capture the complex waveforms and random characteristics of iEEG. To address this critical challenge, this paper introduces NeuroFlowNet, a novel cross-modal generative framework whose core contribution lies in the first-ever reconstruction of iEEG signals from the entire deep temporal lobe region using sEEG signals. NeuroFlowNet is built on Conditional Normalizing Flow (CNF), which directly models complex conditional probability distributions through reversible transformations, thereby explicitly capturing the randomness of brain signals and fundamentally avoiding the pattern collapse issues common in existing generative models. Additionally, the model integrates a multi-scale architecture and self-attention mechanisms to robustly capture fine-grained temporal details and long-range dependencies. Validation results on a publicly available synchronized sEEG-iEEG dataset demonstrate NeuroFlowNet's effectiveness in terms of temporal waveform fidelity, spectral feature reproduction, and functional connectivity restoration. This study establishes a more reliable and scalable new paradigm for non-invasive analysis of deep brain dynamics. The code of this study is available in https://github.com/hdy6438/NeuroFlowNet
Paper Structure (18 sections, 10 equations, 7 figures, 2 tables, 2 algorithms)

This paper contains 18 sections, 10 equations, 7 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Methods
  3. Core Invertible Transformation: Conditional Affine Coupling Layer
  4. Flow Step Composition
  5. Multi-Scale Architecture
  6. Training and Inference
  7. Training
  8. Inference (iEEG Generation)
  9. Experiments
  10. Dataset
  11. Data Preprocessing and Division
  12. Implementation Details
  13. Results and Discussion
  14. Temporal Waveform Comparison
  15. Power Spectral Density Comparison
  16. ...and 3 more sections

Figures (7)

  • Figure 1: Overview of NeuroFlowNet architecture. The model consists of multiple scales, each containing a series of flow steps that transform the iEEG data conditioned on the sEEG data. The final latent variable $Z$ is obtained by concatenating the outputs from all scales.
  • Figure 2: Flow step composition in NeuroFlowNet. Each flow step consists of an invertible $1\times 1$ convolution followed by a conditional affine coupling layer, which transforms the input data while conditioning on the sEEG data.
  • Figure 3: Medial temporal lobe (MTL) depth electrode locations for subjects used in this study (S1, S6 and S9). Each panel shows coronal, sagittal, and axial views of depth electrode trajectories within MTL subregions. Colors indicate anatomical targets: anterior hippocampus (AHL/AHR, red/light red), amygdala (AL/AR, orange/yellow), entorhinal cortex (ECL/ECR, green/light green), parahippocampal gyrus (PHL/PHR, purple/magenta), and lateral rhinal area (LR, blue). Each trajectory consists of eight electrode contacts positioned along a single stereotactic path.
  • Figure 4: Exemplar generated iEEG signals (orange) overlaid with ground truth iEEG signals (blue) for various MTL subregions, specifically showing a 200ms segment from Subject S6. The figure illustrates the performance of NeuroFlowNet in generating iEEG signals from sEEG data across different anatomical targets, including anterior hippocampus (AHL/AHR), amygdala (AL/AR), entorhinal cortex (ECL/ECR), and parahippocampal gyrus (PHL/PHR).
  • Figure 5: Comparison of power spectral density (PSD) between ground truth iEEG signals (blue) and generated iEEG signals (orange) for a 200 ms segment from Subject S6. The figure shows the average PSD across all electrodes within each MTL subregion, including anterior hippocampus (AHL/AHR), amygdala (AL/AR), entorhinal cortex (ECL/ECR), and parahippocampal gyrus (PHL/PHR).
  • ...and 2 more figures