AI Medical Compendium Journal:
Journal of neuroscience methods

Showing 61 to 70 of 161 articles

Virtual EEG-electrodes: Convolutional neural networks as a method for upsampling or restoring channels.

Journal of neuroscience methods
BACKGROUND: In clinical practice, EEGs are assessed visually. For practical reasons, recordings often need to be performed with a reduced number of electrodes and artifacts make assessment difficult. To circumvent these obstacles, different interpola...

An adaptive digital stain separation method for deep learning-based automatic cell profile counts.

Journal of neuroscience methods
BACKGROUND: Quantifying cells in a defined region of biological tissue is critical for many clinical and preclinical studies, especially in the fields of pathology, toxicology, cancer and behavior. As part of a program to develop accurate, precise an...

Grad-CAM helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging.

Journal of neuroscience methods
BACKGROUND: Deep learning using convolutional neural networks (CNNs) has shown great promise in advancing neuroscience research. However, the ability to interpret the CNNs lags far behind, confounding their clinical translation.

A simple Ca-imaging approach to neural network analyses in cultured neurons.

Journal of neuroscience methods
BACKGROUND: Ca-imaging is a powerful tool to measure neuronal dynamics and network activity. To monitor network-level changes in cultured neurons, neuronal activity is often evoked by electrical or optogenetic stimulation and assessed using multi-ele...

Quantitative analysis of brain herniation from non-contrast CT images using deep learning.

Journal of neuroscience methods
BACKGROUND: Brain herniation is one of the fatal outcomes of increased intracranial pressure (ICP). It is caused due to the presence of hematoma or tumor mass in the brain. Ideal midline (iML) divides the healthy brain into two (right and left) nearl...

Topology-guided cyclic brain connectivity generation using geometric deep learning.

Journal of neuroscience methods
BACKGROUND: There is a growing need for analyzing medical data such as brain connectomes. However, the unavailability of large-scale training samples increases risks of model over-fitting. Recently, deep learning (DL) architectures quickly gained mom...

Automatic sleep scoring: A deep learning architecture for multi-modality time series.

Journal of neuroscience methods
BACKGROUND: Sleep scoring is an essential but time-consuming process, and therefore automatic sleep scoring is crucial and urgent to help address the growing unmet needs for sleep research. This paper aims to develop a versatile deep-learning archite...

Myelin detection in fluorescence microscopy images using machine learning.

Journal of neuroscience methods
BACKGROUND: The myelin sheath produced by glial cells insulates the axons, and supports the function of the nervous system. Myelin sheath degeneration causes neurodegenerative disorders, such as multiple sclerosis (MS). There are no therapies for MS ...

Single-trial EEG emotion recognition using Granger Causality/Transfer Entropy analysis.

Journal of neuroscience methods
BACKGROUND: Emotion recognition has been studied for decades, but the classification accuracy needs to be improved.

Denoising Algorithm for Event-Related Desynchronization-Based Motor Intention Recognition in Robot-assisted Stroke Rehabilitation Training with Brain-Machine Interaction.

Journal of neuroscience methods
BACKGROUND: Rehabilitation robots integrated with brain-machine interaction (BMI) can facilitate stroke patients' recovery by closing the loop between motor intention and actual movement. The main challenge is to identify the patient's motor intentio...