AIMC Topic: Brain
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Multi-Modal Electrophysiological Source Imaging With Attention Neural Networks Based on Deep Fusion of EEG and MEG.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jul 11, 2024
The process of reconstructing underlying cortical and subcortical electrical activities from Electroencephalography (EEG) or Magnetoencephalography (MEG) recordings is called Electrophysiological Source Imaging (ESI). Given the complementarity betwee...
Improving brain atrophy quantification with deep learning from automated labels using tissue similarity priors.
Computers in biology and medicine
Jul 10, 2024
Brain atrophy measurements derived from magnetic resonance imaging (MRI) are a promising marker for the diagnosis and prognosis of neurodegenerative pathologies such as Alzheimer's disease or multiple sclerosis. However, its use in individualized ass...
LGGNet: Learning From Local-Global-Graph Representations for Brain-Computer Interface.
IEEE transactions on neural networks and learning systems
Jul 8, 2024
Neuropsychological studies suggest that co-operative activities among different brain functional areas drive high-level cognitive processes. To learn the brain activities within and among different functional areas of the brain, we propose local-glob...
Multi-View Multi-Label Fine-Grained Emotion Decoding From Human Brain Activity.
IEEE transactions on neural networks and learning systems
Jul 8, 2024
Decoding emotional states from human brain activity play an important role in the brain-computer interfaces. Existing emotion decoding methods still have two main limitations: one is only decoding a single emotion category from a brain activity patte...
Evidence that robot-assisted gait training modulates neuroplasticity after stroke: An fMRI pilot study based on graph theory analysis.
Brain research
Jul 6, 2024
OBJECTIVES: To investigate alterations of whole-brain network after stroke and therapeutic mechanisms of robot-assisted gait training (RAGT).
Fast, high-quality, and unshielded 0.2 T low-field mobile MRI using minimal hardware resources.
Magma (New York, N.Y.)
Jul 5, 2024
OBJECTIVE: To propose a deep learning-based low-field mobile MRI strategy for fast, high-quality, unshielded imaging using minimal hardware resources.
Evaluation of 3D T1-weighted spoiled gradient echo MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain.
Neuroradiology
Jul 5, 2024
PURPOSE: To assess image quality and diagnostic confidence of 3D T1-weighted spoiled gradient echo (SPGR) MRI using artificial intelligence (AI) reconstruction.
Comprehensive Review: Machine and Deep Learning in Brain Stroke Diagnosis.
Sensors (Basel, Switzerland)
Jul 4, 2024
Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide expe...
Temporal-spatial cross attention network for recognizing imagined characters.
Scientific reports
Jul 4, 2024
Previous research has primarily employed deep learning models such as Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) for decoding imagined character signals. These approaches have treated the temporal and spatial features ...
Neuroimaging and natural language processing-based classification of suicidal thoughts in major depressive disorder.
Translational psychiatry
Jul 4, 2024
Suicide is a growing public health problem around the world. The most important risk factor for suicide is underlying psychiatric illness, especially depression. Detailed classification of suicide in patients with depression can greatly enhance perso...
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