AIMC Topic: Brain
Clear Filters Showing 571 to 580 of 4188 articles
A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training.
IEEE transactions on neural networks and learning systems
Dec 2, 2024
Brain-computer interfaces (BCIs) provide a direct pathway from the brain to external devices and have demonstrated great potential for assistive and rehabilitation technologies. Endogenous BCIs based on electroencephalogram (EEG) signals, such as mot...
A Bio-Inspired Spiking Attentional Neural Network for Attentional Selection in the Listening Brain.
IEEE transactions on neural networks and learning systems
Dec 2, 2024
Humans show a remarkable ability in solving the cocktail party problem. Decoding auditory attention from the brain signals is a major step toward the development of bionic ears emulating human capabilities. Electroencephalography (EEG)-based auditory...
Brain Network Classification for Accurate Detection of Alzheimer's Disease via Manifold Harmonic Discriminant Analysis.
IEEE transactions on neural networks and learning systems
Dec 2, 2024
Mounting evidence shows that Alzheimer's disease (AD) manifests the dysfunction of the brain network much earlier before the onset of clinical symptoms, making its early diagnosis possible. Current brain network analyses treat high-dimensional networ...
A unique unsupervised enhanced intuitionistic fuzzy C-means for MR brain tissue segmentation.
Scientific reports
Nov 30, 2024
The human-brain is a vital and complicated organ within the body. Identifying brain-related diseases can be challenging. Typically, Magnetic Resonance Imaging (MRI) scanning methods are used to gain insights of the protected regions in the body. Brai...
Self-supervised graph contrastive learning with diffusion augmentation for functional MRI analysis and brain disorder detection.
Medical image analysis
Nov 29, 2024
Resting-state functional magnetic resonance imaging (rs-fMRI) provides a non-invasive imaging technique to study patterns of brain activity, and is increasingly used to facilitate automated brain disorder analysis. Existing fMRI-based learning method...
Riemannian manifold-based disentangled representation learning for multi-site functional connectivity analysis.
Neural networks : the official journal of the International Neural Network Society
Nov 29, 2024
Functional connectivity (FC), derived from resting-state functional magnetic resonance imaging (rs-fMRI), has been widely used to characterize brain abnormalities in disorders. FC is usually defined as a correlation matrix that is a symmetric positiv...
A Novel Real-time Phase Prediction Network in EEG Rhythm.
Neuroscience bulletin
Nov 29, 2024
Closed-loop neuromodulation, especially using the phase of the electroencephalography (EEG) rhythm to assess the real-time brain state and optimize the brain stimulation process, is becoming a hot research topic. Because the EEG signal is non-station...
Denoising low-field MR images with a deep learning algorithm based on simulated data from easily accessible open-source software.
Journal of magnetic resonance (San Diego, Calif. : 1997)
Nov 29, 2024
In this study, we introduce a denoising method aimed at improving the contrast ratio in low-field MRI (LFMRI) using an advanced 3D deep convolutional residual network model. Our approach employs synthetic brain imaging datasets that closely mimic the...
Estimating global phase synchronization by quantifying multivariate mutual information and detecting network structure.
Neural networks : the official journal of the International Neural Network Society
Nov 28, 2024
In neuroscience, phase synchronization (PS) is a crucial mechanism that facilitates information processing and transmission between different brain regions. Specifically, global phase synchronization (GPS) characterizes the degree of PS among multiva...
3D full-dose brain-PET volume recovery from low-dose data through deep learning: quantitative assessment and clinical evaluation.
European radiology
Nov 28, 2024
OBJECTIVES: Low-dose (LD) PET imaging would lead to reduced image quality and diagnostic efficacy. We propose a deep learning (DL) method to reduce radiotracer dosage for PET studies while maintaining diagnostic quality.
Don't Miss the Future of Medicine
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 32555
- Neural Networks, Computer 30510
- Algorithms 27756
- Deep Learning 27309
- Artificial Intelligence 25231
- Robotics 13435
- Image Processing, Computer-Assisted 9895
- Retrospective Studies 9539
- Magnetic Resonance Imaging 6484
- Reproducibility of Results 5747
- Tomography, X-Ray Computed 4963
- Support Vector Machine 4815
- Computational Biology 4399
- Brain 4188
- Computer Simulation 3965
Recent Journals
- Scientific reports 7014
- Sensors (Basel, Switzerland) 4794
- PloS one 4338
- Neural networks : the official journal of the International Neural Network Society 3035
- Computers in biology and medicine 2040
- Computational intelligence and neuroscience 1931
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1395
- IEEE journal of biomedical and health informatics 1201
- Journal of chemical information and modeling 1089
- Nature communications 1072
- Computer methods and programs in biomedicine 895
- Journal of medical Internet research 882
- Bioinformatics (Oxford, England) 882
- IEEE transactions on neural networks and learning systems 859