Neurology

Mental arithmetic task classification with convolutional neural network based on spectral-temporal features from EEG.

In recent years, neuroscientists have been interested to the development of brain-computer interface (BCI) devices. Patients with motor disorders may benefit from BCIs as a means of communication and for the restoration of motor functions. Electroencephalography (EEG) is one of most used for evaluating the neuronal activity. In many computer vision applications, deep neural networks (DNN) show sig...

EEG-GAT: Graph Attention Networks for Classification of Electroencephalogram (EEG) Signals.

Graph neural networks (GNN) are an emerging framework in the deep learning community. In most GNN applications, the graph topology of data samples is provided in the dataset. Specifically, the graph shift operator (GSO), which could be adjacency, graph Laplacian, or their normalizations, is known a priori. However we often have no knowledge of the grand-truth graph topology underlying real-world d...

End-to-end Deep Learning of Polysomnograms for Classification of REM Sleep Behavior Disorder.

Rapid eye movement (REM) sleep behavior disorder (RBD) is parasomnia and a prodromal manifestation of Parkinson's disease. The current diagnostic meth...

A Model Visualization-based Approach for Insight into Waveforms and Spectra Learned by CNNs.

Recent years have shown a growth in the application of deep learning architectures such as convolutional neural networks (CNNs), to electrophysiology ...

EMG Data Augmentation for Grasp Classification Using Generative Adversarial Networks.

Electromyography (EMG) has been used as an interface for the control of robotic hands for decades but with the improvement of embedded electronics and...

Deep Feature Fusion via Graph Convolutional Network for Intracranial Artery Labeling.

Intracranial arteries are critical blood vessels that supply the brain with oxygenated blood. Intracranial artery labels provide valuable guidance and...

Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks.

Early diagnosis and intervention are clinically con-sidered the paramount part of treating cerebral palsy (CP), so it is essential to design an effici...

Classification of Seizure Termination Patterns using Deep Learning on intracranial EEG.

Seizure termination has received significantly less attention than initiation and propagation and consequently, remains a poorly understood phase of s...

A Practical Post-Stroke Elbow Spasticity Assessment Using an Upper Limb Rehabilitation Robot: A Validation Study.

Spasticity is a motor disorder characterised by a velocity-dependent increase in muscle tone, which is critical in neurorehabilitation given its high ...

Probing the link between the APOE-ε4 allele and whole-brain gray matter using deep learning.

The APOE-ε4 allele is a known genetic risk for Alzheimer's disease (AD). Thus, it can be reasoned that the APOE-ε4 allele would also impact neurodegen...

A sEMG Proportional Control for the Gripper of Patient Side Manipulator in da Vinci Surgical System.

There is a large community of people with hand disabilities, and these disabilities can be a barrier to those looking to retain or pursue surgical car...

Cross-Validation of Machine Learning Models for the Functional Outcome Prediction after Post-Stroke Robot-Assisted Rehabilitation.

The state of the art is still lacking an extensive analysis of which clinical characteristics are leading to better outcomes after robot-assisted reha...

The Effectiveness of Narrowing the Window size for LD & HD EMG Channels based on Novel Deep Learning Wavelet Scattering Transform Feature Extraction Approach.

The use of the Electromyogram (EMG) signals as a source of control to command externally powered prostheses is often challenged by the signal complexi...

Towards a Closed-loop Neuro-Robotic Approach to DBS Electrode Implantation based on Real-Time Wrist Rigidity Evaluation.

The iHandU system is a wearable device that quantitatively evaluates changes in wrist rigidity during Deep Brain Stimulation (DBS) surgery, allowing c...

Automatic and Personalized Adaptation of Therapy Parameters for Unsupervised Robot-Assisted Rehabilitation: a Pilot Evaluation.

Growing evidence shows that increasing the dose of upper limb therapy after stroke might improve functional outcomes and unsupervised robot-assisted t...

AGREE: an upper-limb robotic platform for personalized rehabilitation, concept and clinical study design.

Rehabilitation exoskeletons can supplement therapist-based training allowing post-stroke patients to perform functional, high-dosage, repetitive exerc...

Comparison of Machine Learning Techniques for Activities of Daily Living Classification with Electromyographic Data.

Advances in data science and wearable robotic devices present an opportunity to improve rehabilitation outcomes. Some of these devices incorporate ele...

Detection thresholds for electrostimulation combined with robotic leg support in sub-acute stroke patients.

Stroke is one of the leading causes of disability in adults in the European Union. It often leads to motor impairments, such as a hemiparetic lower ex...

Improving Ankle Muscle Recruitment via Plantar Pressure Biofeedback during Robot Resisted Gait Training in Cerebral Palsy.

Neurological impairment from stroke or cerebral palsy often presents with diminished ankle plantar flexor function during the propulsive phase of gait...

Measuring Torque Production with a Robotic Exoskeleton during Cervical Transcutaneous Spinal Stimulation.

Spinal cord injury (SCI) affects a large number of individuals in the United States. Unfortunately, traditional neurorehabilitation therapy leaves out...