Reconstruction of natural images from human fMRI using a three-stage multi-level deep fusion model

The TS-ML-DFM method proposed in this study, when applied to decoding brain visual patterns using fMRI data, has outperformed previous algorithms, thereby facilitating further advancements in research within this field.

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Publication Details

Journal
Journal of Neuroscience Methods
Published
2024-08-31
DOI
https://doi.org/10.1016/j.jneumeth.2024.110269
Citations
2
Primary Topic
Image Processing Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.64
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article

Reconstruction of natural images from human fMRI using a three-stage multi-level deep fusion model

Lu Meng, Zhenxuan Tang, Yangqian Liu
2 citations
Journal of Neuroscience Methods
Image Processing Techniques and Applications
0.64
article

Reconstruction of natural images from human fMRI using a three-stage multi-level deep fusion model

Lu Meng, Zhenxuan Tang, Yangqian Liu
article en
2 citations

Abstract

The TS-ML-DFM method proposed in this study, when applied to decoding brain visual patterns using fMRI data, has outperformed previous algorithms, thereby facilitating further advancements in research within this field.

Journal of Neuroscience MethodsVol. 411
Peng Cheng Laboratory (CN), Northeastern University (CN)
Openalex Percentile: Top 27%
Image Processing Techniques and Applications
0.64
DOIOpen Access PDFOpenAlex
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Reconstruction of natural images from human fMRI using a three-stage multi-level deep fusion model — Lu Meng, Zhenxuan Tang, et al. · Journal of Neuroscience Methods (2024) | TGRS Research Map | TGRS