Neurology

Multimodal Emotion Recognition from Eye Image, Eye Movement and EEG Using Deep Neural Networks.

In consideration of the complexity of recording electroencephalography(EEG), some researchers are trying to find new features of emotion recognition. In order to investigate the potential of eye tracking glasses for multimodal emotion recognition, we collect and use eye images to classify five emotions along with eye movements and EEG. We compare four combinations of the three different types of d...

Deep Learning of Motor Imagery EEG Classification for Brain-Computer Interface Illiterate Subject.

BCI illiterate subject is defined as the subject who cannot achieve accuracy higher than 70%. BCI illiterate subject cannot produce stronger contralateral ERD/ERS activity, thus most of the frequency band-based algorithms cannot obtain higher accuracy. Deep learning with convolutional neural networks (CNN) has revolutionized in many recent studies to learn features and classify different types of ...

Classification of Movement Direction From Electroencephalogram During Working Memory Time.

When humans perform cognitive tasks, it is necessary to hold information temporarily. This is done by a brain function called working memory (WM). Sin...

Automated Process Incorporating Machine Learning Segmentation and Correlation of Oral Diseases with Systemic Health.

Imaging fluorescent disease biomarkers in tissues and skin is a non-invasive method to screen for health conditions. We report an automated process th...

Representation Learning of 3D Brain Angiograms, an Application for Cerebral Vasospasm Prediction.

Stroke is the fifth leading cause of death in the United States. Subarachnoid hemorrhage (SAH) is a type of stroke often caused by the spontaneous rup...

Probabilistic prediction of Epileptic Seizures using SVM.

In this paper, an algorithm based on the linear Support Vector Machine (SVM) tool was proposed to classify intracranial electroencephalography (iEEG) ...

A 3D Deep Residual Convolutional Neural Network for Differential Diagnosis of Parkinsonian Syndromes on F-FDG PET Images.

Idiopathic Parkinsons disease and atypical parkinsonian syndromes have similar symptoms at early disease stages, which makes the early differential di...

Early Parkinson's Disease Detection via Touchscreen Typing Analysis using Convolutional Neural Networks.

Parkinson's Disease (PD) is the second most common neurodegenerative disorder worldwide, causing both motor and non-motor symptoms. In the early stage...

Automatic Classification and Monitoring of Denovo Parkinson's Disease by Learning Demographic and Clinical Features.

Parkinson's Disease (PD) is the second most prevalent progressive neurological disorder around the world with high incidence rates for seniors. Since ...

Deep Learning with Convolutional Neural Network for detecting microsleep states from EEG: A comparison between the oversampling technique and cost-based learning.

Any occupation which involves critical decision making in real-time requires attention and concentration. When repetitive and expanded working periods...

3D Convolutional Neural Networks for Event-Related Potential detection.

Deep learning techniques have recently been successful in the classification of brain evoked responses for multiple applications, including brain-mach...

Wearable Sensors for Prodromal Motor Assessment of Parkinson's Disease using Supervised Learning.

Parkinson's disease (PD) is a common neurodegenerative disorder characterized by disabling motor and non-motor symptoms. Idiopathic hyposmia (IH), a r...

Multimodal Data Fusion of Deep Learning and Dynamic Functional Connectivity Features to Predict Alzheimer's Disease Progression.

Early prediction of diseased brain conditions is critical for curing illness and preventing irreversible neuronal dysfunction and loss. Generically re...

Comparison of Extreme Learning Machine and K-Nearest Neighbour Performance in Classifying EEG Signal of Normal, Poor and Capable Dyslexic Children.

Dyslexia is a specific learning difficulty associated with brain capability in processing numbers and letters. Analysis of Electroencephalogram (EEG) ...

EEG Signals Classification Using Machine Learning for The Identification and Diagnosis of Schizophrenia.

This paper presents the design of a machine learning-based classifier for the differentiation between Schizophrenia patients and healthy controls usin...

3D Ultrasound Spine Image Selection Using Convolution Learning-to-Rank Algorithm.

3D Ultrasound imaging has become an important means of scoliosis assessment as it is a real-time, cost-effective and radiation-free imaging technique....

Automatic detection of High Frequency Oscillations (80-500Hz) based on Convolutional Neural Network in Human Intracerebral Electroencephalogram.

Recently, high-frequency oscillations (HFOs) of range 80-500 Hz in electroencephalogram (EEG) recordings of epilepsy patients are considered as a reli...

EEG-Based Classification of Olfactory Response to Pleasant Stimuli.

Olfactory perception involves complex processing distributed along several cortical and sub-cortical regions in the brain. Although several studies ha...

Integration of Forearm sEMG Signals with IMU Sensors for Trajectory Planning and Control of Assistive Robotic Arm.

Patients with issues such as cerebral palsy, spinal cord injury, and multiple sclerosis have difficulties with activities of daily living (ADL). Their...

Designing Configurable Arm Rehabilitation Games: How Do Different Game Elements Affect User Motion Trajectories?

For successful rehabilitation of a patient after a stroke or traumatic brain injury, it is crucial that rehabilitation activities are motivating, prov...