Neurology

EEG Tensorization Enhances CNN-Based Outcome Classification in Comatose Patients Following a Cardiac Arrest.

Standard diagnostic methods for evaluating the severity of brain injuries resulting from cardiac arrest, such as the Glasgow Coma Scale, exhibit subjective biases that lead to potentially fatal misclassifications, where life-support systems are prematurely withdrawn from patients who might otherwise recover. This study utilizes an open dataset from the International Cardiac Arrest Research Consort...

Contrastive Self-supervised EEG Representation Learning for Emotion Classification.

Self-supervised learning provides an effective approach to leverage a large amount of unlabeled data. Numerous previous studies have indicated that applying self-supervision to physiological signals can yield better representations of the signals. In the paper, we aim to apply this method to the crucial field of emotion recognition. We perform the experiment with several state-of-the-art contrasti...

Dual Attention Graph Convolutional Network Fusing Imaging and Genetic Data for Early Alzheimer's Disease Diagnosis.

Alzheimer's Disease (AD) poses a significant global neurodegenerative challenge, underscoring the urgency of early clinical intervention. Our paper pr...

Unsupervised Hybrid Deep Feature Encoder for Robust Feature Learning from Resting-State EEG Data.

EEG classification is a challenging task due to the nonstationary nature of EEG data and the covariance shift induced by cross-subject variance. Recen...

Sleep structure discriminates patients with isolated REM sleep behavior disorder: a deep learning approach.

Rapid eye movement (REM) sleep behavior disorder (RBD) is a disorder characterized by increased muscle tone and dream-enactment behaviors in REM sleep...

EEG Acquisition and Motor Imagery Classification for Robotic Control.

The adoption of brain-computer interfaces (BCIs) has significantly increased in various application domains, particularly in the field of controlling ...

Neurocognitive assessment under various human-robot teaming environments.

Human-robot teaming has become increasingly important with the advent of intelligent machines. Prior efforts suggest that performance, mental workload...

Challenging Deep Learning Methods for EEG Signal Denoising under Data Corruption.

Capturing informative electroencephalogram (EEG) signals is a challenging task due to the presence of noise (e.g., due to human movement). In extreme ...

TAU-DI Net: A Multi-Scale Convolutional Network Combining Prob-Sparse Attention for EEG-based Depression Identification.

EEG-based detection of major depression disorder (MDD) plays a pivotal role in the subsequent treatment and recovery. With the rapid development of de...

An Efficient Deep Transfer Learning Network for Characterization of Stroke Patients' Motor Execution from Multi-Channel EEG-Recordings.

Recent advances in stroke rehabilitation technology have been focused on developing Intelligent Rehabilitation Robots (IRR) that can effectively engag...

Complexity Analysis based on Parietal Fuzzy Entropy to Facilitate ADHD Diagnosis in Young Children.

Attention deficit hyperactivity disorder (ADHD) is the most common condition affecting the development of neurons in children. Therefore, early and ac...

Decoding Visual Perception from EEG Using Explainable Graph Neural Network.

Brain decoding is an emerging area in the fields of neuroscience and machine learning. The goal of decoding is to utilize measured brain activity to u...

Mapping Cognitive Engagement: EEG and Graph Theory Analysis of Brain Region Involvement in Supernumerary Robotic Finger Utilization.

As the worldwide incidence of stroke increases, supernumerary robotic limbs (SRLs), more specifically supernumerary robotic fingers (SRFs), present a ...

3D probe localization from 2D ultrasound images using an RFF-enhanced deep neural network.

Peripheral nerve blocking (PNB) via ultrasound (US) imaging offers the advantages of non-invasiveness, nonionizing radiation, and real-time visualizat...

Chin EMG Scalogram-Based Deep CNN for OSA Screening.

Obstructive Sleep Apnea (OSA) is a common sleep condition characterized by frequent pauses in breathing caused by the relaxation of muscles in the upp...

A nested cross validation approach to machine learning model performance evaluation on a small dataset for Creutzfeldt-Jakob disease diagnosis.

The use of machine learning (ML) to diagnose neurological diseases has become increasingly popular. However, some rare neurodegenerative diseases such...

Enhancing Model Generalizability In Parkinson's Disease Automatic Assessment: A Semi-Supervised Approach Across Independent Experiments.

Machine learning in Parkinson's disease assessment uses data from clinically-coded movements, such as finger tapping, to objectively measure motor imp...

Interpretable SincNet-Based Spatiotemporal Neural Network for Seizure Prediction.

Spatiotemporal convolutional neural networks (CNNs) have emerged as potent tools for seizure prediction (SP) using electroencephalogram (EEG) signals,...

Hip Hiking Gait Improvement with Electrohydraulic Robotic Knee: Preliminary Results.

Individuals with Transfemoral Amputation (TFA) usually exhibit hip hiking walking with passive prostheses due to insufficient knee flexion. Powered kn...

Evaluating Augmentation Approaches for Deep Learning-based Major Depressive Disorder Diagnosis with Raw Electroencephalogram Data.

While deep learning methods are increasingly applied in research contexts for neuropsychiatric disorder diagnosis, small dataset size limits their pot...