Neurology

A Critical Test of Deep Convolutional Neural Networks' Ability to Capture Recurrent Processing in the Brain Using Visual Masking.

Recurrent processing is a crucial feature in human visual processing supporting perceptual grouping, figure-ground segmentation, and recognition under challenging conditions. There is a clear need to incorporate recurrent processing in deep convolutional neural networks, but the computations underlying recurrent processing remain unclear. In this article, we tested a form of recurrence in deep res...

Development and Validation of a Deep Learning Model for Predicting Treatment Response in Patients With Newly Diagnosed Epilepsy.

IMPORTANCE: Selection of antiseizure medications (ASMs) for epilepsy remains largely a trial-and-error approach. Under this approach, many patients have to endure sequential trials of ineffective treatments until the "right drugs" are prescribed.

Bilateral Nerve-Sparing Robot-Assisted Retroperitoneal Lymph Node Dissection: A Minimally Invasive Approach.

Prediction of Post Stroke recovery: Artificial intelligence could be a key of success.

GSTCNet: Gated spatio-temporal correlation network for stroke mortality prediction.

Stroke continues to be the most common cause of death in China. It has great significance for mortality prediction for stroke patients, especially in ...

Robotic subxiphoid-optical thymectomy.

Robot-assisted thymectomy through a subxiphoid scopic approach can provide a good surgical view, similar to that of median sternotomy. We originally u...

The Future of Minimally Invasive Spinal Surgery.

Strong forces are pushing minimally invasive spinal surgery (MISS) to the forefront of spine care. Less-invasive surgical techniques have been enabled...

MEMD-HHT based Emotion Detection from EEG using 3D CNN.

In this study, the Multivariate Empirical Mode Decomposition (MEMD) is applied to multichannel EEG to obtain scale-aligned intrinsic mode functions (I...

A novel deep learning approach using AlexNet for the classification of electroencephalograms in Alzheimer's Disease and Mild Cognitive Impairment.

Alzheimer's Disease (AD) is the most common form of dementia. Mild Cognitive Impairment (MCI) is the term given to the stage describing prodromal AD a...

A Time-Series Augmentation Method Based on Empirical Mode Decomposition and Integrated LSTM Neural Network.

Adequate patients' data have always been critical for disease assessment. However, large amounts of patient data are often difficult to collect, espec...

Label Alignment Improves EEG-based Machine Learning-based Classification of Traumatic Brain Injury.

Machine learning and deep learning algorithms have paved the way for improved analysis of biomedical data which has led to a better understanding of v...

A streamable large-scale clinical EEG dataset for Deep Learning.

Deep Learning has revolutionized various fields, including Computer Vision, Natural Language Processing, as well as Biomedical research. Within the fi...

CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patients.

Clinical outcome prediction plays an important role in stroke patient management. From a machine learning point-of-view, one of the main challenges is...

A Pruned Deep Learning Approach for Classification of Motor Imagery Electroencephalography Signals.

The Deep Learning (DL) approach has been gaining much popularity in recent years in the development of electroencephalogram (EEG) based Motor Imagery ...

A Deep Learning Framework Based on Dynamic Channel Selection for Early Classification of Left and Right Hand Motor Imagery Tasks.

Ideal brain-computer interfaces (BCIs) need to be efficient and accurate, demanding for classifiers that can work across subjects while providing high...

Design of a 6-DoF Cost-effective Differential-drive based Robotic system for Upper-Limb Stroke Rehabilitation.

This paper discusses the design, construction, and characteristics of a six degree of freedom (6-DoF) robotic upper limb stroke rehabilitation device....

Robot Assisted Neurosurgery for High-Accuracy, Minimally-Invasive Deep Brain Electrophysiology in Monkeys.

Traditional methods to access subcortical structures involve the use of anatomical atlases and high precision stereotaxic frames but suffer from signi...

A Riemannian Deep Learning Representation to Describe Gait Parkinsonian Locomotor Patterns.

Parkinson is the second most common neurodegenerative disease, mainly related to progressive locomotor alterations caused by dopamine deficiency. The ...

A Cross-modality Deep Learning Method for Measuring Decision Confidence from Eye Movement Signals.

Electroencephalography (EEG) signals can effectively measure the level of human decision confidence. However, it is difficult to acquire EEG signals i...

An MR-Conditional Needle Driver for Robot-Assisted Spinal Injections: Design Modifications and Evaluations.

This paper introduces design modifications to our MR-Conditional, 2-degree-of-freedom (DOF), remotely-actuated needle driver for MRI-guided spinal inj...