AIMC Topic: Brain

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Brain Topology Modeling With EEG-Graphs for Auditory Spatial Attention Detection.

IEEE transactions on bio-medical engineering
OBJECTIVE: Despite recent advances, the decoding of auditory attention from brain signals remains a challenge. A key solution is the extraction of discriminative features from high-dimensional data, such as multi-channel electroencephalography (EEG)....

A Brain-Controlled Quadruped Robot: A Proof-of-Concept Demonstration.

Sensors (Basel, Switzerland)
Coupling brain-computer interfaces (BCIs) and robotic systems in the future can enable seamless personal assistant systems in everyday life, with the requests that can be performed in a discrete manner, using one's brain activity only. These types of...

Deep learning applications in vascular dementia using neuroimaging.

Current opinion in psychiatry
PURPOSE OF REVIEW: Vascular dementia (VaD) is the second common cause of dementia after Alzheimer's disease, and deep learning has emerged as a critical tool in dementia research. The aim of this article is to highlight the current deep learning appl...

Distinctive approach in brain tumor detection and feature extraction using biologically inspired DWT method and SVM.

Scientific reports
Brain tumors result from uncontrolled cell growth, potentially leading to fatal consequences if left untreated. While significant efforts have been made with some promising results, the segmentation and classification of brain tumors remain challengi...

Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms.

Scientific reports
The clinical applications of brain age prediction have expanded, particularly in anticipating the onset and prognosis of various neurodegenerative diseases. In the current study, we proposed a deep learning algorithm that leverages brain structural i...

Machine learning and deep learning for brain tumor MRI image segmentation.

Experimental biology and medicine (Maywood, N.J.)
Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic resonance imaging (MRI) is a commonly used imaging technique for capturi...

Electrophysiological brain imaging based on simulation-driven deep learning in the context of epilepsy.

NeuroImage
Identifying the location, the spatial extent and the electrical activity of distributed brain sources in the context of epilepsy through ElectroEncephaloGraphy (EEG) recordings is a challenging task because of the highly ill-posed nature of the under...

Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions.

PLoS biology
Models that predict brain responses to stimuli provide one measure of understanding of a sensory system and have many potential applications in science and engineering. Deep artificial neural networks have emerged as the leading such predictive model...

Deep learning MRI-only synthetic-CT generation for pelvis, brain and head and neck cancers.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: MRI-only planning relies on dosimetrically accurate synthetic-CT (sCT) generation to allow dose calculation. Here we validated the dosimetric accuracy of sCTs generated using a deep learning algorithm for pelvic, brain and hea...

Cell-type-directed design of synthetic enhancers.

Nature
Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes. It has been a long-standing goal in the field to decode the regulatory logic of an enhan...