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- W4367016470 abstract "Electroencephalography (EEG) is a non-invasive method widely used in neuroscience and clinical research. The process of reconstructing cortex activities from EEG signals is called EEG source imaging (ESI). Source imaging is a highly underdetermined process. Most of the traditional source imaging methods use prior information to add appropriate constraints to obtain a unique estimation. In recent years, a source imaging method based on artificial neural networks has been developed, which uses artificial neural networks to train a model that can reconstruct cortex activities from EEG signals. This paper proposes a transformer encoder network based ESI method and tests it on simulated data. The numerical results and imaging results show that the transformer encoder network achieves good performance in the case of different metrics and different numbers of extended sources." @default.
- W4367016470 created "2023-04-27" @default.
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- W4367016470 date "2023-02-24" @default.
- W4367016470 modified "2023-10-01" @default.
- W4367016470 title "EEG Source Imaging based on a Transformer Encoder Network" @default.
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- W4367016470 doi "https://doi.org/10.1109/nnice58320.2023.10105793" @default.
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