Enhancing Brain Source Reconstruction through Physics-Informed 3D Neural Networks

• URL: http://arxiv.org/abs/2411.00143v1
• Date: Thu, 31 Oct 2024 18:43:38 GMT
• Title: Enhancing Brain Source Reconstruction through Physics-Informed 3D Neural Networks
• Authors: Marco Morik, Ali Hashemi, Klaus-Robert Müller, Stefan Haufe, Shinichi Nakajima,
• Abstract summary: We propose the novel hybrid method 3D-PIUNet for EEG source localization. Our model significantly improves spatial accuracy, demonstrating superior performance over both traditional and end-to-end data-driven methods. We validate our findings with real EEG data from a visual task, where 3D-PIUNet successfully identifies the visual cortex.
• Score: 17.023015853478043
• License:
• Abstract: Reconstructing brain sources is a fundamental challenge in neuroscience, crucial for understanding brain function and dysfunction. Electroencephalography (EEG) signals have a high temporal resolution. However, identifying the correct spatial location of brain sources from these signals remains difficult due to the ill-posed structure of the problem. Traditional methods predominantly rely on manually crafted priors, missing the flexibility of data-driven learning, while recent deep learning approaches focus on end-to-end learning, typically using the physical information of the forward model only for generating training data. We propose the novel hybrid method 3D-PIUNet for EEG source localization that effectively integrates the strengths of traditional and deep learning techniques. 3D-PIUNet starts from an initial physics-informed estimate by using the pseudo inverse to map from measurements to source space. Secondly, by viewing the brain as a 3D volume, we use a 3D convolutional U-Net to capture spatial dependencies and refine the solution according to the learned data prior. Training the model relies on simulated pseudo-realistic brain source data, covering different source distributions. Trained on this data, our model significantly improves spatial accuracy, demonstrating superior performance over both traditional and end-to-end data-driven methods. Additionally, we validate our findings with real EEG data from a visual task, where 3D-PIUNet successfully identifies the visual cortex and reconstructs the expected temporal behavior, thereby showcasing its practical applicability.

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