AIMC Topic: Brain

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Ensemble Support Vector Recurrent Neural Network for Brain Signal Detection.

IEEE transactions on neural networks and learning systems
The brain-computer interface (BCI) P300 speller analyzes the P300 signals from the brain to achieve direct communication between humans and machines, which can assist patients with severe disabilities to control external machines or robots to complet...

Toward Deep Adaptive Hinging Hyperplanes.

IEEE transactions on neural networks and learning systems
The adaptive hinging hyperplane (AHH) model is a popular piecewise linear representation with a generalized tree structure and has been successfully applied in dynamic system identification. In this article, we aim to construct the deep AHH (DAHH) mo...

Qutrit-Inspired Fully Self-Supervised Shallow Quantum Learning Network for Brain Tumor Segmentation.

IEEE transactions on neural networks and learning systems
Classical self-supervised networks suffer from convergence problems and reduced segmentation accuracy due to forceful termination. Qubits or bilevel quantum bits often describe quantum neural network models. In this article, a novel self-supervised s...

Application value of T2 fluid-attenuated inversion recovery sequence based on deep learning in static lacunar infarction.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Regular monitoring of static lacunar infarction (SLI) lesions plays an important role in preventing disease development and managing prognosis. Magnetic resonance imaging is one method used to monitor SLI lesions.

NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images.

IEEE transactions on visualization and computer graphics
We introduce NeuroConstruct, a novel end-to-end application for the segmentation, registration, and visualization of brain volumes imaged using wide-field microscopy. NeuroConstruct offers a Segmentation Toolbox with various annotation helper functio...

Deep learning-based diagnosis of Alzheimer's disease using brain magnetic resonance images: an empirical study.

Scientific reports
The limited accessibility of medical specialists for Alzheimer's disease (AD) can make obtaining an accurate diagnosis in a timely manner challenging and may influence prognosis. We investigated whether VUNO Med-DeepBrain AD (DBAD) using a deep learn...

Explaining neural activity in human listeners with deep learning via natural language processing of narrative text.

Scientific reports
Deep learning (DL) approaches may also inform the analysis of human brain activity. Here, a state-of-art DL tool for natural language processing, the Generative Pre-trained Transformer version 2 (GPT-2), is shown to generate meaningful neural encodin...

Brain Age Prediction/Classification through Recurrent Deep Learning with Electroencephalogram Recordings of Seizure Subjects.

Sensors (Basel, Switzerland)
With modern population growth and an increase in the average lifespan, more patients are becoming afflicted with neurodegenerative diseases such as dementia and Alzheimer's. Patients with a history of epilepsy, drug abuse, and mental health disorders...

A multicohort geometric deep learning study of age dependent cortical and subcortical morphologic interactions for fluid intelligence prediction.

Scientific reports
The relationship of human brain structure to cognitive function is complex, and how this relationship differs between childhood and adulthood is poorly understood. One strong hypothesis suggests the cognitive function of Fluid Intelligence (Gf) is de...

An artificial-intelligence-based age-specific template construction framework for brain structural analysis using magnetic resonance images.

Human brain mapping
It is an essential task to construct brain templates and analyze their anatomical structures in neurological and cognitive science. Generally, templates constructed from magnetic resonance imaging (MRI) of a group of subjects can provide a standard r...