Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1111/pcn.13478 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Accuracy of a Rapid-Response EEG's Automated Seizure-Burden Estimator: AccuRASE Study.
Neurology
January 2025
Neurology, Yale School of Medicine, New Haven, CT.
Background And Objectives: The use of rapid response EEG (rr-EEG) has recently expanded in limited-resource settings and as a supplement to conventional EEG to rapidly detect and treat nonconvulsive status epilepticus. The study objective was to test the accuracy of an rr-EEG's automated seizure burden estimator (ASBE).
Methods: This is a retrospective observational study using multiple blinded reviewers.
3D convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery EEG signal.
Front Neurorobot
December 2024
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China.
Non-invasive brain-computer interfaces (BCI) hold great promise in the field of neurorehabilitation. They are easy to use and do not require surgery, particularly in the area of motor imagery electroencephalography (EEG). However, motor imagery EEG signals often have a low signal-to-noise ratio and limited spatial and temporal resolution.
View Article and Find Full Text PDFLateralizing Value of Artificial Intelligence-Based Segmentation Software in MRI-Negative Focal Epilepsy.
J Epilepsy Res
December 2024
Department of Neurology, Seoul National University College of Medicine, Seoul, Korea.
Background And Purpose: The magnetic resonance images (MRIs) ability of lesion detection in epilepsy is crucial for a diagnosis and surgical outcome. Using automated artificial intelligence (AI)-based tools for measuring cortical thickness and brain volume originally developed for dementia, we aimed to identify whether it could lateralize epilepsy with normal MRIs.
Methods: Non-lesional 3-Tesla MRIs of 428 patients diagnosed with focal epilepsy, based on semiology and electroencephalography findings, were analyzed.
An attention-based motor imagery brain-computer interface system for lower limb exoskeletons.
Rev Sci Instrum
December 2024
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.
Lower-limb exoskeletons have become increasingly popular in rehabilitation to help patients with disabilities regain mobility and independence. Brain-computer interface (BCI) offers a natural control method for these exoskeletons, allowing users to operate them through their electroencephalogram (EEG) signals. However, the limited EEG decoding performance of the BCI system restricts its application for lower limb exoskeletons.
View Article and Find Full Text PDFDecoding of movement-related cortical potentials at different speeds.
Cogn Neurodyn
December 2024
Hangzhou Innovation Institute, Beihang University, Hangzhou, 310052 Zhejiang China.
The decoding of electroencephalogram (EEG) signals, especially motion-related cortical potentials (MRCP), is vital for the early detection of motor intent before movement execution. To enhance the decoding accuracy of MRCP and promote the application of early motion intention in active rehabilitation training, we propose a method for decoding MRCP signals. Specifically, an experimental paradigm is designed for the efficient capture of MRCP signals.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!