Eeg diffuse source imaging method based on autoencoder and spatial difference sparse constraint

By employing an EEG diffusion source imaging method based on autoencoders and spatial difference sparse constraints, the problems of real-time EEG source imaging and brain source size reconstruction are solved, achieving more efficient brain source imaging.

CN117243614BActive Publication Date: 2026-07-24CHONGQING UNIV OF POSTS & TELECOMM
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHONGQING UNIV OF POSTS & TELECOMM
Filing Date
2023-10-30
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing EEG source imaging methods have poor real-time performance, traditional methods require complex prior assumptions and are computationally complex, and deep learning methods cannot accurately reconstruct brain source size information.

Method used

An EEG diffusion source imaging method based on autoencoder and spatial difference sparse constraints is adopted. Through autoencoder training and loss function optimization, combined with the temporal and spatial information of EEG data and brain source signals, brain source data is generated using boundary element method and region growing method, and the loss function is optimized to reconstruct brain source signals.

Benefits of technology

It improves the real-time performance and accuracy of EEG source imaging, enabling better reconstruction of the size and location information of brain sources, and is suitable for brain science research and diagnosis of neurological diseases.

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Abstract

The application belongs to the field of biomedical imaging, and particularly relates to an EEG diffuse source imaging method based on a self-encoder and spatial difference sparse constraint, which comprises the following steps: brain modeling, calculation of a lead matrix, generation of simulated brain source signals by using a region growing method; taking the simulated brain source signals as inputs of a self-encoder, inversing the cortical brain source signals through the self-encoder, and outputting reconstructed electroencephalogram signals; calculating a loss function, and optimizing network parameters through back propagation; visualizing the reconstructed brain source signals; the loss function is composed of three parts, the first part is a mean square error between simulated and reconstructed electroencephalogram data, the second part is a mean square error and a mean absolute error between simulated and reconstructed brain source signals, and the third part is a spatial difference sparse regularization term of the brain source signals; through joint optimization of the three parts, accurate and robust cortical diffuse source imaging estimation is obtained, and technical support is provided for the field of neural disease diagnosis and brain-computer interface.
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