Neurology

Mixed-reality assistive robotic power chair simulator for Parkinson's tremor testing.

This note describes the development of a mixed-reality assistive robotic wheel chair simulator for testing of Parkinson's tremor mitigation and operator assistance. It consists of a power chair (PCh), roller dynamometer, head mounted virtual reality (VR) display, computer with VR game engine, and microcontroller to interface the PCh and computer. Unlike past VR PCh simulators, both a tremor notch ...

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states.

Predicting biomedical outcomes from Magnetoencephalography and Electroencephalography (M/EEG) is central to applications like decoding, brain-computer-interfaces (BCI) or biomarker development and is facilitated by supervised machine learning. Yet, most of the literature is concerned with classification of outcomes defined at the event-level. Here, we focus on predicting continuous outcomes from M...

Machine learning to quantify habitual physical activity in children with cerebral palsy.

AIM: To investigate whether activity-monitors and machine learning models could provide accurate information about physical activity performed by chil...

Macroscale and microcircuit dissociation of focal and generalized human epilepsies.

Thalamo-cortical pathology plays key roles in both generalized and focal epilepsies, but there is little work directly comparing these syndromes at th...

Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning.

Achieving the shift towards Industry 4.0 is only feasible through the active integration of the shopfloor into the transformation process. Several sho...

SS-SWT and SI-CNN: An Atrial Fibrillation Detection Framework for Time-Frequency ECG Signal.

Atrial fibrillation is the most common arrhythmia and is associated with high morbidity and mortality from stroke, heart failure, myocardial infarctio...

Commentary: Robotic-Re-Thymectomy: A Surgical Effective Chance to Treat Refractory Myasthenia Gravis.

An automated machine learning approach to predict brain age from cortical anatomical measures.

The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, whic...

EEG Signal and Feature Interaction Modeling-Based Eye Behavior Prediction Research.

In recent years, with the development of brain science and biomedical engineering, as well as the rapid development of electroencephalogram (EEG) sign...

Deep Learning model for markerless tracking in spinal SBRT.

Stereotactic Body Radiation Therapy (SBRT), alternatively termed Stereotactic ABlative Radiotherapy (SABR) or Stereotactic RadioSurgery (SRS), deliver...

AxoNet: A deep learning-based tool to count retinal ganglion cell axons.

In this work, we develop a robust, extensible tool to automatically and accurately count retinal ganglion cell axons in optic nerve (ON) tissue images...

Identifying drugs with disease-modifying potential in Parkinson's disease using artificial intelligence and pharmacoepidemiology.

PURPOSE: The aim of the study was to assess the feasibility of an approach combining computational methods and pharmacoepidemiology to identify potent...

Rehabilitation of older people with Parkinson's disease: an innovative protocol for RCT study to evaluate the potential of robotic-based technologies.

BACKGROUND: Parkinson's disease is one of the most frequent causes of disability among the older adults. It is a chronic-progressive neuro-degenerativ...

Usefulness of deep learning-assisted identification of hyperdense MCA sign in acute ischemic stroke: comparison with readers' performance.

PURPOSE: To evaluate the usefulness of deep learning-assisted diagnosis for identifying hyperdense middle cerebral artery sign (HMCAS) on non-contrast...

EEG classification across sessions and across subjects through transfer learning in motor imagery-based brain-machine interface system.

Transfer learning enables the adaption of models to handle mismatches of distributions across sessions or across subjects. In this paper, we proposed ...

Machine learning-assisted neurotoxicity prediction in human midbrain organoids.

INTRODUCTION: Brain organoids are highly complex multi-cellular tissue proxies, which have recently risen as novel tools to study neurodegenerative di...

Digital conversations about suicide among teenagers and adults with epilepsy: A big-data, machine learning analysis.

OBJECTIVE: Digital media conversations can provide important insight into the concerns and struggles of people with epilepsy (PWE) outside of formal c...

Cross-Subject Seizure Detection in EEGs Using Deep Transfer Learning.

Electroencephalography (EEG) plays an import role in monitoring the brain activities of patients with epilepsy and has been extensively used to diagno...

Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning.

This study attempted to multimodally measure mental workload and validate indicators for estimating mental workload. A simulated computer work compose...

Time-resolved correspondences between deep neural network layers and EEG measurements in object processing.

The ventral visual stream is known to be organized hierarchically, where early visual areas processing simplistic features feed into higher visual are...