AIMC Topic: Brain

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Resolving the neural mechanism of core object recognition in space and time: A computational approach.

Neuroscience research
The underlying mechanism of object recognition- a fundamental brain ability- has been investigated in various studies. However, balancing between the speed and accuracy of recognition is less explored. Most of the computational models of object recog...

Teleoperation control of a wheeled mobile robot based on Brain-machine Interface.

Mathematical biosciences and engineering : MBE
This paper presents a novel teleoperation system using Electroencephalogram (EEG) to control the motion of a wheeled mobile robot (WMR). Different from the other traditional motion controlling method, the WMR is braked with the EEG classification res...

Deriving a robust deep-learning model for subcortical brain segmentation by using a large-scale database: Preprocessing, reproducibility, and accuracy of volume estimation.

NMR in biomedicine
Increasing the accuracy and reproducibility of subcortical brain segmentation is advantageous in various related clinical applications. In this study, we derived a segmentation method based on a convolutional neural network (i.e., U-Net) and a large-...

A Joint Group Sparsity-based deep learning for multi-contrast MRI reconstruction.

Journal of magnetic resonance (San Diego, Calif. : 1997)
Multi-contrast magnetic resonance imaging (MRI) can provide richer diagnosis information. The data acquisition time, however, is increased than single-contrast imaging. To reduce this time, k-space undersampling is an effective way but a smart recons...

Brain-Controlled 2D Navigation Robot Based on a Spatial Gradient Controller and Predictive Environmental Coordinator.

IEEE journal of biomedical and health informatics
OBJECTIVE: Brain-computer interfaces (BCIs) have been used in two-dimensional (2D) navigation robotic devices, such as brain-controlled wheelchairs and brain-controlled vehicles. However, contemporary BCI systems are driven by binary selective contro...

Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools.

Computers in biology and medicine
BACKGROUND: Brain cancer is a destructive and life-threatening disease that imposes immense negative effects on patients' lives. Therefore, the detection of brain tumors at an early stage improves the impact of treatments and increases the patients s...

Characterizing physiological high-frequency oscillations using deep learning.

Journal of neural engineering
Intracranially-recorded interictal high-frequency oscillations (HFOs) have been proposed as a promising spatial biomarker of the epileptogenic zone. However, HFOs can also be recorded in the healthy brain regions, which complicates the interpretation...

A Systematic Review of Virtual Reality and Robot Therapy as Recent Rehabilitation Technologies Using EEG-Brain-Computer Interface Based on Movement-Related Cortical Potentials.

Biosensors
To enhance the treatment of motor function impairment, patients' brain signals for self-control as an external tool may be an extraordinarily hopeful option. For the past 10 years, researchers and clinicians in the brain-computer interface (BCI) fiel...

Deep Learning-Powered Bessel-Beam Multiparametric Photoacoustic Microscopy.

IEEE transactions on medical imaging
Enabling simultaneous and high-resolution quantification of the total concentration of hemoglobin ( [Formula: see text]), oxygen saturation of hemoglobin (sO2), and cerebral blood flow (CBF), multi-parametric photoacoustic microscopy (PAM) has emerge...

Deep problems with neural network models of human vision.

The Behavioral and brain sciences
Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more ...