Neurology

T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs

Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, such BCIs usually require a subject-specific calibration session before each use, which is time-consuming and user-unfriendly. Transfer learning (TL) has been proposed to shorten or el...

Robust Feature Engineering Techniques for Designing Efficient Motor Imagery-Based BCI-Systems

A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-Computer Interface (BCI) technology exhibit promise for improving motor rehabilitation outcomes. The intricate nature of EEG data poses a significant hurdle for current BCI systems. Recently, a qualitative repository of EEG signals tied to both upper and lower limb execution of motor a...

A Powered Prosthetic Hand with Vision System for Enhancing the Anthropopathic Grasp

The anthropomorphism of grasping process significantly benefits the experience and grasping efficiency of prosthetic hand wearers. Currently, prosth...

Hyperbolic embedding of brain networks can predict the surgery outcome in temporal lobe epilepsy

Epilepsy surgery, particularly for temporal lobe epilepsy (TLE), remains a vital treatment option for patients with drug-resistant seizures. However...

Parkinson's Disease Diagnosis Through Deep Learning: A Novel LSTM-Based Approach for Freezing of Gait Detection

Deep learning holds tremendous potential in healthcare for uncovering hidden patterns within extensive clinical datasets, aiding in the diagnosis of...

Towards Brain Passage Retrieval -- An Investigation of EEG Query Representations

Information Retrieval (IR) systems primarily rely on users' ability to translate their internal information needs into (text) queries. However, this...

BATseg: Boundary-aware Multiclass Spinal Cord Tumor Segmentation on 3D MRI Scans

Spinal cord tumors significantly contribute to neurological morbidity and mortality. Precise morphometric quantification, encompassing the size, loc...

A Combined Channel Approach for Decoding Intracranial EEG Signals: Enhancing Accuracy through Spatial Information Integration

Intracranial EEG (iEEG) recording, characterized by high spatial and temporal resolution and superior signal-to-noise ratio (SNR), enables the devel...

Not All Errors Are Equal: Investigation of Speech Recognition Errors in Alzheimer's Disease Detection

Automatic Speech Recognition (ASR) plays an important role in speech-based automatic detection of Alzheimer's disease (AD). However, recognition err...

Leveraging Prompt Learning and Pause Encoding for Alzheimer's Disease Detection

Compared to other clinical screening techniques, speech-and-language-based automated Alzheimer's disease (AD) detection methods are characterized by...

A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases

Graph neural networks (GNNs) are powerful machine learning models designed to handle irregularly structured data. However, their generic design ofte...

LVS-Net: A Lightweight Vessels Segmentation Network for Retinal Image Analysis

The analysis of retinal images for the diagnosis of various diseases is one of the emerging areas of research. Recently, the research direction has ...

STEAM-EEG: Spatiotemporal EEG Analysis with Markov Transfer Fields and Attentive CNNs

Electroencephalogram (EEG) signals play a pivotal role in biomedical research and clinical applications, including epilepsy diagnosis, sleep disorde...

Early Diagnosis of Alzheimer's Diseases and Dementia from MRI Images Using an Ensemble Deep Learning

Alzheimer's Disease (AD) is a progressive neurological disorder that can result in significant cognitive impairment and dementia. Accurate and timel...

Biological Brain Age Estimation using Sex-Aware Adversarial Variational Autoencoder with Multimodal Neuroimages

Brain aging involves structural and functional changes and therefore serves as a key biomarker for brain health. Combining structural magnetic reson...

Self-Supervised Masked Mesh Learning for Unsupervised Anomaly Detection on 3D Cortical Surfaces

Unsupervised anomaly detection in brain imaging is challenging. In this paper, we propose self-supervised masked mesh learning for unsupervised anom...

Accurate early detection of Parkinson's disease from SPECT imaging through Convolutional Neural Networks

Early and accurate detection of Parkinson's disease (PD) is a crucial diagnostic challenge carrying immense clinical significance, for effective tre...

Reconstructing Quantitative Cerebral Perfusion Images Directly From Measured Sinogram Data Acquired Using C-arm Cone-Beam CT

To shorten the door-to-puncture time for better treating patients with acute ischemic stroke, it is highly desired to obtain quantitative cerebral p...

Spinal ligaments detection on vertebrae meshes using registration and 3D edge detection

Spinal ligaments are crucial elements in the complex biomechanical simulation models as they transfer forces on the bony structure, guide and limit ...

Reconstruction of 3D lumbar spine models from incomplete segmentations using landmark detection

Patient-specific 3D spine models serve as a foundation for spinal treatment and surgery planning as well as analysis of loading conditions in biomec...