AIMC Topic: Brain

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Neuromorphic one-shot learning utilizing a phase-transition material.

Proceedings of the National Academy of Sciences of the United States of America Apr 17, 2024
Design of hardware based on biological principles of neuronal computation and plasticity in the brain is a leading approach to realizing energy- and sample-efficient AI and learning machines. An important factor in selection of the hardware building ...
Learning Neurons Neural Networks, Computer Computers Brain
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Convolutional spiking neural networks for intent detection based on anticipatory brain potentials using electroencephalogram.

Scientific reports Apr 17, 2024
Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition o...
Intention Neural Networks, Computer Humans Brain Electroencephalography
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A GRU-CNN model for auditory attention detection using microstate and recurrence quantification analysis.

Scientific reports Apr 17, 2024
Attention as a cognition ability plays a crucial role in perception which helps humans to concentrate on specific objects of the environment while discarding others. In this paper, auditory attention detection (AAD) is investigated using different dy...
Humans Neural Networks, Computer Machine Learning Brain Brain Mapping Electroencephalography Attention
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SPINNED: Simulation-based physics-informed neural network for deconvolution of dynamic susceptibility contrast MRI perfusion data.

Magnetic resonance in medicine Apr 16, 2024
PURPOSE: To propose the simulation-based physics-informed neural network for deconvolution of dynamic susceptibility contrast (DSC) MRI (SPINNED) as an alternative for more robust and accurate deconvolution compared to existing methods.
Humans Neural Networks, Computer Image Processing, Computer-Assisted Contrast Media Magnetic Resonance Imaging Algorithms Brain Computer Simulation Signal-To-Noise Ratio
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Knowledge-driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un-supervised learning.

Magnetic resonance in medicine Apr 16, 2024
Deep learning (DL) has emerged as a leading approach in accelerating MRI. It employs deep neural networks to extract knowledge from available datasets and then applies the trained networks to reconstruct accurate images from limited measurements. Unl...
Supervised Machine Learning Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Humans Neural Networks, Computer Algorithms Brain
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Highly-accelerated CEST MRI using frequency-offset-dependent k-space sampling and deep-learning reconstruction.

Magnetic resonance in medicine Apr 16, 2024
PURPOSE: To develop a highly accelerated CEST Z-spectral acquisition method using a specifically-designed k-space sampling pattern and corresponding deep-learning-based reconstruction.
Magnetic Resonance Imaging Female Prospective Studies Adult Brain Humans Male Brain Neoplasms Retrospective Studies Algorithms Deep Learning Image Processing, Computer-Assisted
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Accurate and robust segmentation of cerebral vasculature on four-dimensional arterial spin labeling magnetic resonance angiography using machine-learning approach.

Magnetic resonance imaging Apr 16, 2024
Segmentation of cerebral vasculature on MR vascular images is of great significance for clinical application and research. However, the existing cerebrovascular segmentation approaches are limited due to insufficient image contrast and complicated al...
Cerebrovascular Circulation Spin Labels Cerebral Arteries Female Humans Imaging, Three-Dimensional Adult Machine Learning Image Processing, Computer-Assisted Algorithms Male Magnetic Resonance Angiography Brain
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Differentiating ischemic stroke patients from healthy subjects using a large-scale, retrospective EEG database and machine learning methods.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association Apr 16, 2024
OBJECTIVES: We set out to develop a machine learning model capable of distinguishing patients presenting with ischemic stroke from a healthy cohort of subjects. The model relies on a 3-min resting electroencephalogram (EEG) recording from which featu...
Male Middle Aged Case-Control Studies Signal Processing, Computer-Assisted Machine Learning Aged Brain Waves Diagnosis, Computer-Assisted Databases, Factual Aged, 80 and over Predictive Value of Tests Time Factors Brain Adult Ischemic Stroke Retrospective Studies Diagnosis, Differential Humans Female Reproducibility of Results Electroencephalography
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Editorial for the Special Issue "Sensing Brain Activity Using EEG and Machine Learning".

Sensors (Basel, Switzerland) Apr 15, 2024
Sensing brain activity to reveal, analyze and recognize brain activity patterns has become a topic of great interest and ongoing research [...].
Brain Electroencephalography Humans Machine Learning
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Results of the 2023 ISBI challenge to reduce GABA-edited MRS acquisition time.

Magma (New York, N.Y.) Apr 13, 2024
PURPOSE: Use a conference challenge format to compare machine learning-based gamma-aminobutyric acid (GABA)-edited magnetic resonance spectroscopy (MRS) reconstruction models using one-quarter of the transients typically acquired during a complete sc...
Signal-To-Noise Ratio Magnetic Resonance Spectroscopy gamma-Aminobutyric Acid Machine Learning Humans Computer Simulation Algorithms Neural Networks, Computer Deep Learning Brain Image Processing, Computer-Assisted
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