XDL-ESI: Electrophysiological Sources Imaging via explainable deep learning framework with validation on simultaneous EEG and iEEG

Jiao, Meng; Xian, Xiaochen; Wang, Boyu; Zhang, Yu; Yang, Shihao; Chen, Spencer; Sun, Hai; Liu, Feng

doi:10.1016/j.neuroimage.2024.120802

NeuroImage

2024

DOI: 10.1016/j.neuroimage.2024.120802

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XDL-ESI: Electrophysiological Sources Imaging via explainable deep learning framework with validation on simultaneous EEG and iEEG

Meng Jiao,

Xiaochen Xian,

Boyu Wang

et al.

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Comprehensive analysis of supervised learning methods for electrical source imaging

Reynaud,

Merlini,

Ben Salem

et al. 2024

Front. Neurosci.

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Electroencephalography source imaging (ESI) is an ill-posed inverse problem: an additional constraint is needed to find a unique solution. The choice of this constraint, or prior, remains a challenge for most ESI methods. This work explores the application of supervised learning methods for spatio-temporal ESI, where the relationship between measurements and sources is learned directly from the data. Three neural networks were trained on synthetic data and compared with non-learning based methods. Two distinct types of simulation, each based on different models of brain electrical activity, were employed to quantitatively assess the generalization capabilities of the neural networks and the impact of training data on their performances, using five complementary metrics. The results demonstrate that, with appropriately designed simulations, neural networks can be competitive with non-learning-based approaches, even when applied to previously unseen data.

show abstract

Comprehensive analysis of supervised learning methods for electrical source imaging

Reynaud,

Merlini,

Ben Salem

et al. 2024

Front. Neurosci.

View full text Add to dashboard Cite

show abstract

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XDL-ESI: Electrophysiological Sources Imaging via explainable deep learning framework with validation on simultaneous EEG and iEEG

Cited by 1 publication

References 60 publications

Comprehensive analysis of supervised learning methods for electrical source imaging

Comprehensive analysis of supervised learning methods for electrical source imaging

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