Neurology

Nonlinear effective connectivity measure based on adaptive Neuro Fuzzy Inference System and Granger Causality.

Exploring brain networks is an essential step towards understanding functional organization of the brain, which needs characterization of linear and nonlinear connections based on measurements like EEG or MEG. Conventional measures of connectivity are mostly linear and bivariate. This paper proposes an effective connectivity measure called Adaptive Neuro-Fuzzy Inference System Granger Causality (A...

Multilevel Feature Representation of FDG-PET Brain Images for Diagnosing Alzheimer's Disease.

Using a single imaging modality to diagnose Alzheimer's disease (AD) or mild cognitive impairment (MCI) is a challenging task. FluoroDeoxyGlucose Positron Emission Tomography (FDG-PET) is an important and effective modality used for that purpose. In this paper, we develop a novel method by using single modality (FDG-PET) but multilevel feature, which considers both region properties and connectivi...

Neuro-cognitive mechanisms of global Gestalt perception in visual quantification.

Recent neuroimaging studies identified posterior regions in the temporal and parietal lobes as neuro-functional correlates of subitizing and global Ge...

Analysis and Simulation of Capacitor-Less ReRAM-Based Stochastic Neurons for the in-Memory Spiking Neural Network.

The stochastic neuron is a key for event-based probabilistic neural networks. We propose a stochastic neuron using a metal-oxide resistive random-acce...

Robot-assisted training using Hybrid Assistive Limb® for cerebral palsy.

PURPOSE: The Hybrid Assistive Limb® (HAL®, CYBERDYNE) is a wearable robot that provides assistance to a patient while they are walking, standing, and ...

Sensor Fusion for Myoelectric Control Based on Deep Learning With Recurrent Convolutional Neural Networks.

Electromyogram (EMG) signal decoding is the essential part of myoelectric control. However, traditional machine learning methods lack the capability o...

Language processing from the perspective of electrical stimulation mapping.

Electrical Stimulation (ES) is a neurostimulation technique that is used to localize language functions in the brain of people with intractable epilep...

MEG-BMI to Control Phantom Limb Pain.

A brachial plexus root avulsion (BPRA) causes intractable pain in the insensible affected hands. Such pain is partly due to phantom limb pain, which i...

Global temporal lobe asymmetry as a semi-quantitative imaging biomarker for temporal lobe epilepsy lateralization: A machine learning classification study.

OBJECTIVE: The purpose of this study was to evaluate the utility of global semi-quantitative analysis via fluorine-18-flurodeoxyglucose positron emiss...

Machine learning detects EEG microstate alterations in patients living with temporal lobe epilepsy.

PURPOSE: Quasi-stable electrical distribution in EEG called microstates could carry useful information on the dynamics of large scale brain networks. ...

Deep learning for detection of focal epileptiform discharges from scalp EEG recordings.

OBJECTIVE: Visual assessment of the EEG still outperforms current computer algorithms in detecting epileptiform discharges. Deep learning is a promisi...

Unsupervised Machine Learning to Identify High Likelihood of Dementia in Population-Based Surveys: Development and Validation Study.

BACKGROUND: Dementia is increasing in prevalence worldwide, yet frequently remains undiagnosed, especially in low- and middle-income countries. Popula...

Outcomes of Intradetrusor OnabotulinumtoxinA Injection in Adults with Congenital Spinal Dysraphism in Tertiary Transitional Urology Clinic.

PURPOSE: Published data regarding intradetrusor injection of onabotulinumtoxinA in adults with congenital spinal dysraphism are scarce. In this study,...

The effects of error-augmentation versus error-reduction paradigms in robotic therapy to enhance upper extremity performance and recovery post-stroke: a systematic review.

Despite upper extremity function playing a crucial role in maintaining one's independence in activities of daily living, upper extremity impairments r...

Introducing chaos behavior to kernel relevance vector machine (RVM) for four-class EEG classification.

This paper addresses a chaos kernel function for the relevance vector machine (RVM) in EEG signal classification, which is an important component of B...

Assisting hand function after spinal cord injury with a fabric-based soft robotic glove.

BACKGROUND: Spinal cord injury is a devastating condition that can dramatically impact hand motor function. Passive and active assistive devices are b...

O 089 - A soft robotic exosuit assisting the paretic ankle in patients post-stroke: Effect on muscle activation during overground walking.

This study compared overground walking with and without exosuit assistance in post-stroke patients. Exosuit-assisted walking was found to improve pare...

Transductive Joint-Knowledge-Transfer TSK FS for Recognition of Epileptic EEG Signals.

Intelligent recognition of electroencephalogram (EEG) signals is an important means to detect seizure. Traditional methods for recognizing epileptic E...

Translation of robot-assisted rehabilitation to clinical service: a comparison of the rehabilitation effectiveness of EMG-driven robot hand assisted upper limb training in practical clinical service and in clinical trial with laboratory configuration for chronic stroke.

BACKGROUND: Rehabilitation robots can provide intensive physical training after stroke. However, variations of the rehabilitation effects in translati...

Time-Varying EEG Correlations Improve Automated Neonatal Seizure Detection.

The aim of this study was to develop methods for detecting the nonstationary periodic characteristics of neonatal electroencephalographic (EEG) seizur...