ArticleComputational intelligence and neuroscience2021
Design of Deep Learning Model for Task-Evoked fMRI Data Classification.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Real-Time EEG Decoding of Motor Imagery via Nonlinear Dimensionality Reduction (Manifold Learning) and Shallow Classifiers.Biosensors · 2025Article
- DSAM: A deep learning framework for analyzing temporal and spatial dynamics in brain networks.Medical image analysis · 2025Article
- Characterizing dynamic functional connectivity subnetwork contributions in narrative classification with Shapley values.Network neuroscience (Cambridge, Mass.) · 2025Article
- Article
- High-accuracy machine learning techniques for functional connectome fingerprinting and cognitive state decoding.Human brain mapping · 2023Article
- On the benefits of self-taught learning for brain decoding.GigaScience · 2022Article
- Decoding Task-Based fMRI Data with Graph Neural Networks, Considering Individual Differences.Brain sciences · 2022Article
- Neural Decoding of EEG Signals with Machine Learning: A Systematic Review.Brain sciences · 2021Review
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3 authors.
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Abstract
Machine learning methods have been successfully applied to neuroimaging signals, one of which is to decode specific task states from functional magnetic resonance imaging (fMRI) data. In this paper, we propose a model that simultaneously utilizes characteristics of both spatial and temporal sequential information of fMRI data with deep neural networks to classify the fMRI task states. We designed a convolution network module and a recurrent network module to extract the spatial and temporal features of fMRI data, respectively. In particular, we also add the attention mechanism to the recurrent network module, which more effectively highlights the brain activation state at the moment of reaction. We evaluated the model using task-evoked fMRI data from the Human Connectome Project (HCP) dataset, the classification accuracy got 94.31%, and the experimental results have shown that the model can effectively distinguish the brain states under different task stimuli.
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