Neurology

Optimizing Real-Time MI-BCI Performance in Post-Stroke Patients: Impact of Time Window Duration on Classification Accuracy and Responsiveness.

Brain-computer interfaces (BCIs) are promising tools for motor neurorehabilitation. Achieving a balance between classification accuracy and system responsiveness is crucial for real-time applications. This study aimed to assess how the duration of time windows affects performance, specifically classification accuracy and the false positive rate, to optimize the temporal parameters of MI-BCI system...

Progression to refractory status epilepticus: A machine learning analysis by means of classification and regression tree analysis.

BACKGROUND AND OBJECTIVES: to identify predictors of progression to refractory status epilepticus (RSE) using a machine learning technique.

Artificial intelligence enhancements in the neurosurgical treatment of tuberous sclerosis.

An enzyme-inspired specificity in deep learning model for sleep stage classification using multi-channel PSG signals input: Separating training approach and its performance on cross-dataset validation for generalizability.

Numerous automatic sleep stage classification systems have been developed, but none have become effective assistive tools for sleep technicians due to...

Effects of high-definition tDCS targeting individual motor hotspot with EMG-driven robotic hand training on upper extremity motor function: a pilot randomized controlled trial.

BACKGROUND: Delivering HD-tDCS on individual motor hotspot with optimal electric fields could overcome challenges of stroke heterogeneity, potentially...

Integrating Large Language Model, EEG, and Eye-Tracking for Word-Level Neural State Classification in Reading Comprehension.

With the recent proliferation of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), there has been a significant shift i...

Factors associated with 90-day mortality in Vietnamese stroke patients: Prospective findings compared with explainable machine learning, multicenter study.

The prevalence and predictors of mortality following an ischemic stroke or intracerebral hemorrhage have not been well established among patients in V...

Mass Spectrometry Structural Proteomics Enabled by Limited Proteolysis and Cross-Linking.

The exploration of protein structure and function stands at the forefront of life science and represents an ever-expanding focus in the development of...

Robotic Assisted Transcranial Doppler Monitoring in Acute Neurovascular Care: A Feasibility and Safety Study.

BACKGROUND: Transcranial color Doppler (TCD) is currently the only noninvasive bedside tool capable of providing real-time information on cerebral hem...

Integrated Ultrasound-Enrichment and Machine Learning in Colorimetric Lateral Flow Assay for Accurate and Sensitive Clinical Alzheimer's Biomarker Diagnosis.

The colloidal gold nanoparticle (AuNP)-based colorimetric lateral flow assay (LFA) is one of the most promising analytical tools for point-of-care dis...

An Intersubject Brain-Computer Interface Based on Domain-Adversarial Training of Convolutional Neural Network.

OBJECTIVE: Attention decoding plays a vital role in daily life, where electroencephalography (EEG) has been widely involved. However, training a unive...

Application Value of a Machine Learning Model in Predicting Mild Depression Associated with Migraine without Aura.

To investigate the application value of a machine learning model in predicting mild depression associated with migraine without aura (MwoA). 178 pat...

OxcarNet: sinc convolutional network with temporal and channel attention for prediction of oxcarbazepine monotherapy responses in patients with newly diagnosed epilepsy.

Monotherapy with antiepileptic drugs (AEDs) is the preferred strategy for the initial treatment of epilepsy. However, an inadequate response to the in...

Efficacy of robot-assisted gait training on lower extremity function in subacute stroke patients: a systematic review and meta-analysis.

BACKGROUND: Robot-Assisted Gait Training (RAGT) is a novel technology widely employed in the field of neurological rehabilitation for patients with su...

Text mining of verbal autopsy narratives to extract mortality causes and most prevalent diseases using natural language processing.

Verbal autopsy (VA) narratives play a crucial role in understanding and documenting the causes of mortality, especially in regions lacking robust medi...

Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.

BACKGROUND: Recent advances in multivariate pattern recognition have fostered the search for reliable neuroimaging-based biomarkers in psychiatric con...

PD-ARnet: a deep learning approach for Parkinson's disease diagnosis from resting-state fMRI.

. The clinical diagnosis of Parkinson's disease (PD) relying on medical history, clinical symptoms, and signs is subjective and lacks sensitivity. Res...

Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal electroencephalography.

Brain resection is curative for a subset of patients with drug resistant epilepsy but up to half will fail to achieve sustained seizure freedom in the...

Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces.

Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their ...

A Machine Learning Approach to Concussion Risk Estimation Among Players Exhibiting Visible Signs in Professional Hockey.

BACKGROUND: The identification of concussion risk factors, such as visible signs and mechanisms of injury, improves concussion identification. Explori...