Visual image reconstructed without semantics from human brain activity using linear image decoders and nonlinear noise suppression

In recent years, substantial strides have been made in the field of visual image reconstruction, particularly in its capacity to generate high-quality visual representations from human brain activity while considering semantic information. This advancement not only enables the recreation of visual c...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Cognitive neurodynamics Ročník 19; číslo 1; s. 20
Hlavní autor: Li, Qiang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.12.2025
Springer Nature B.V
Témata:
Accuracy
Activity patterns
Artificial Intelligence
Artificial neural networks
Biochemistry
Biomedical and Life Sciences
Biomedicine
Brain
Brain mapping
Brain research
Cognitive Psychology
Computer Science
Datasets
Decoders
Decoding
Deep learning
Heart rate
Image processing
Image quality
Image reconstruction
Information processing
Mapping
Memory
Neural networks
Neuroimaging
Neurosciences
Noise reduction
Nonlinear response
Physiology
Research Article
Semantics
Structure-function relationships
Visual fields
Visual perception
Linear decoding
Deep autoencoder denoised neural networks
Functional similarities
Brain activity
Visual image reconstruction
Neural explanatory
ISSN:1871-4080, 1871-4099
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In recent years, substantial strides have been made in the field of visual image reconstruction, particularly in its capacity to generate high-quality visual representations from human brain activity while considering semantic information. This advancement not only enables the recreation of visual content but also provides valuable insights into the intricate processes occurring within high-order functional brain regions, contributing to a deeper understanding of brain function. However, considering fusion semantics in reconstructing visual images from brain activity involves semantic-to-image guide reconstruction and may ignore underlying neural computational mechanisms, which does not represent true reconstruction from brain activity. In response to this limitation, our study introduces a novel approach that combines linear mapping with nonlinear noise suppression to reconstruct visual images perceived by subjects based on their brain activity patterns. The primary challenge associated with linear mapping lies in its susceptibility to noise interference. To address this issue, we leverage a flexible denoised deep convolutional neural network, which can suppress noise from linear mapping. Our investigation encompasses linear mapping as well as the training of shallow and deep autoencoder denoised neural networks, including a pre-trained, state-of-the-art denoised neural network. The outcome of our study reveals that combining linear image decoding with nonlinear noise reduction significantly enhances the quality of reconstructed images from human brain activity. This suggests that our methodology holds promise for decoding intricate perceptual experiences directly from brain activity patterns without semantic information. Moreover, the model has strong neural explanatory power because it shares structural and functional similarities with the visual brain.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1871-4080
1871-4099
DOI:10.1007/s11571-024-10184-z