Neurology

Determination of Saccade Latency Distributions using Video Recordings from Consumer-grade Devices.

Quantitative and accurate tracking of neurocognitive decline remains an ongoing challenge. We seek to address this need by focusing on robust and unobtrusive measurement of saccade latency - the time between the presentation of a visual stimulus and the initiation of an eye movement towards the stimulus - which has been shown to be altered in patients with neurocognitive decline or neurodegenerati...

A machine learning approach to targeted balance rehabilitation in people with Parkinson's disease using a sparse sensor set.

Clinical Balance Assessments Often Rely On Functional Tasks As A Proxy For Balance (E.G., Timed Up And Go). In Contrast, Analyses Of Balance In Research Settings Incorporate Quantitative Biomechanical Measurements (E.G., Whole-Body Angular Momentum, H) Using Motion Capture Techniques. Fully Instrumenting Patients In The Clinic Is Not Feasible, And Thus It Is Desirable To Estimate Biomechanical Qua...

Automatic Sleep Stage Classification Using Single-Channel EEG: Learning Sequential Features with Attention-Based Recurrent Neural Networks.

We propose in this work a feature learning approach using deep bidirectional recurrent neural networks (RNNs) with attention mechanism for single-chan...

A Biomechatronic EPP upper-limb prosthesis controller and its performance comparison to other topologies.

Historically, Classic Extended Physiological Proprioception (EPP) as an upper-limb prosthesis control topology has been outperforming functionally all...

Improving EEG-Based Motor Imagery Classification via Spatial and Temporal Recurrent Neural Networks.

Motor imagery (MI) based Brain-Computer Interface (BCI) is an important active BCI paradigm for recognizing movement intention of severely disabled pe...

Convolutional Neural Network for Target Face Detection using Single-trial EEG Signal.

Face recognition plays an import role in our daily lives. However, computer face recognition performance degrades dramatically with the presence of va...

Hardware Implementation of a Performance and Energy-optimized Convolutional Neural Network for Seizure Detection.

We present for the first time a μW-power convolutional neural network for seizure detection running on a low-power microcontroller. On a dataset of 22...

Energetics during robot-assisted training predicts recovery in stroke.

Clinical investigators have asserted patients should be active participants in the therapy process in stroke rehabilitation. While robotics introduces...

Deep Learning Enabled Automatic Abnormal EEG Identification.

In hospitals, physicians diagnose brain-related disorders such as epilepsy by analyzing electroencephalograms (EEG). However, manual analysis of EEG d...

A Velocity-Based Flow Field Control Approach for Reshaping Movement of Stroke-Impaired Individuals with a Lower-Limb Exoskeleton.

This paper describes a controller for guiding and assisting leg movement during walking with a lower limb exoskeleton with actuated hip and knee joint...

Ensemble learning based on overlapping clusters of subjects to predict microsleep states from EEG.

Microsleeps are brief and involuntary instances of complete loss of sleep-related consciousness. We present a novel approach of creating overlapping c...

Towards Robot-Based Cognitive and Motor Assessment Across the HIV-Stroke Spectrum.

There is an increasing population of people living with both HIV and stroke around the world with no effective neurorehabilitation strategies to deal ...

Investigating Upper Limb Movement Classification on Users with Tetraplegia as a Possible Neuroprosthesis Interface.

Spinal cord injury (SCI), stroke and other nervous system conditions can result in partial or total paralysis of individual's limbs. Numerous technolo...

Improving Young Stroke Prediction by Learning with Active Data Augmenter in a Large-Scale Electronic Medical Claims Database.

Electronic medical claims (EMC) database has been successfully used for predicting occurrences of stroke and a variety of other diseases. However, ina...

Surface EMG Pattern Recognition Using Long Short-Term Memory Combined with Multilayer Perceptron.

Motion classification based on pattern recognition of surface EMG (sEMG) signals is a promising approach for prosthetic control. We present a pattern ...

Investigating the Impact of CNN Depth on Neonatal Seizure Detection Performance.

This study presents a novel, deep, fully convolutional architecture which is optimized for the task of EEG-based neonatal seizure detection. Architect...

A Unified Optic Nerve Head and Optic Cup Segmentation Using Unsupervised Neural Networks for Glaucoma Screening.

Segmentation of retinal anatomical features such as optic nerve head (ONH) and optic cup, the brightest area in the center of ONH which is devoid of n...

O-GlcNAcPRED-II: an integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K-means PCA oversampling technique.

MOTIVATION: Protein O-GlcNAcylation (O-GlcNAc) is an important post-translational modification of serine (S)/threonine (T) residues that involves mult...

Effectiveness of a single session of dual-transcranial direct current stimulation in combination with upper limb robotic-assisted rehabilitation in chronic stroke patients: a randomized, double-blind, cross-over study.

The impact of transcranial direct current stimulation (tDCS) is controversial in the neurorehabilitation literature. It has been suggested that tDCS s...

Retinal Nerve Fiber Layer Features Identified by Unsupervised Machine Learning on Optical Coherence Tomography Scans Predict Glaucoma Progression.

PURPOSE: To apply computational techniques to wide-angle swept-source optical coherence tomography (SS-OCT) images to identify novel, glaucoma-related...