ArticleBrain sciences2022
fMRI Brain Decoding and Its Applications in Brain-Computer Interface: A Survey.
Article in Brain sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
13 citing papers in PubMed, 54 citations in OpenAlex.
- NeuroStream: spectral-spatio-temporal deep learning for visual stimulus classification from EEG.Scientific reports · 2026Article
- fNIRS dataset of motor hand-gripping activity using the NIRSport2 system.Neurophotonics · 2026Article
- Symbiotic brain-machine drawing via visual brain-computer interfaces.npj biomedical innovations · 2026Article
- DCBAN: A Dynamic Confidence Bayesian Adaptive Network for Reconstructing Visual Images from fMRI Signals.Brain sciences · 2025Article
- TSFNet: Temporal-Spatial Fusion Network for Hybrid Brain-Computer Interface.Sensors (Basel, Switzerland) · 2025Article
- Advances in complex thinking and neurotechnologies in education: a bibliometric analysis of research trends.Cognitive processing · 2025Review
- Integration of brain-computer interfaces with sacral nerve stimulation: a vision for closed-loop, volitional control of bladder function in neurogenic patients through real-time cortical signal modulation and peripheral neuro-stimulation.World journal of urology · 2025Review
- Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations.Diagnostics (Basel, Switzerland) · 2025Review
- Unique Cortical and Subcortical Activation Patterns for Different Conspecific Calls in Marmosets.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Electroencephalography-Based Brain-Computer Interfaces in Rehabilitation: A Bibliometric Analysis (2013-2023).Sensors (Basel, Switzerland) · 2024Review
- Natural Image Reconstruction from fMRI Based on Node-Edge Interaction and Multi-Scale Constraint.Brain sciences · 2024Article
- Through their eyes: Multi-subject brain decoding with simple alignment techniques.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Decoding Visual fMRI Stimuli from Human Brain Based on Graph Convolutional Neural Network.Brain sciences · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Brain neural activity decoding is an important branch of neuroscience research and a key technology for the brain-computer interface (BCI). Researchers initially developed simple linear models and machine learning algorithms to classify and recognize brain activities. With the great success of deep learning on image recognition and generation, deep neural networks (DNN) have been engaged in reconstructing visual stimuli from human brain activity via functional magnetic resonance imaging (fMRI). In this paper, we reviewed the brain activity decoding models based on machine learning and deep learning algorithms. Specifically, we focused on current brain activity decoding models with high attention: variational auto-encoder (VAE), generative confrontation network (GAN), and the graph convolutional network (GCN). Furthermore, brain neural-activity-decoding-enabled fMRI-based BCI applications in mental and psychological disease treatment are presented to illustrate the positive correlation between brain decoding and BCI. Finally, existing challenges and future research directions are addressed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.