[HTML][HTML] A novel semi-supervised meta learning method for subject-transfer brain–computer interface
The brain–computer interface (BCI) provides a direct communication pathway between the
human brain and external devices. However, the models trained for existing subjects …
human brain and external devices. However, the models trained for existing subjects …
Efficient and generalizable cross-patient epileptic seizure detection through a spiking neural network
Introduction Epilepsy is a global chronic disease that brings pain and inconvenience to
patients, and an electroencephalogram (EEG) is the main analytical tool. For clinical aid that …
patients, and an electroencephalogram (EEG) is the main analytical tool. For clinical aid that …
Multi-view cross-subject seizure detection with information bottleneck attribution
Y Zhao, G Zhang, Y Zhang, T Xiao… - Journal of Neural …, 2022 - iopscience.iop.org
Objective. Significant progress has been witnessed in within-subject seizure detection from
electroencephalography (EEG) signals. Consequently, more and more works have been …
electroencephalography (EEG) signals. Consequently, more and more works have been …
Cross-patient automatic epileptic seizure detection using patient-adversarial neural networks with spatio-temporal EEG augmentation
Z Zhang, T Ji, M Xiao, W Wang, G Yu, T Lin… - … Signal Processing and …, 2024 - Elsevier
Cross-patient automatic epileptic seizure detection through electroencephalogram (EEG) is
significant for clinical application and research. However, most automatic seizure detection …
significant for clinical application and research. However, most automatic seizure detection …
Efficient EEG Feature Learning Model Combining Random Convolutional Kernel with Wavelet Scattering for Seizure Detection.
Y Liu, Y Jiang, J Liu, J Li, M Liu, W Nie… - International Journal of …, 2024 - europepmc.org
Automatic seizure detection has significant value in epilepsy diagnosis and treatment.
Although a variety of deep learning models have been proposed to automatically learn …
Although a variety of deep learning models have been proposed to automatically learn …
Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning …
Y Statsenko, V Babushkin, T Talako, T Kurbatova… - Biomedicines, 2023 - mdpi.com
Deep learning (DL) is emerging as a successful technique for automatic detection and
differentiation of spontaneous seizures that may otherwise be missed or misclassified …
differentiation of spontaneous seizures that may otherwise be missed or misclassified …
Time-Series Anomaly Detection Based on Dynamic Temporal Graph Convolutional Network for Epilepsy Diagnosis
G Wu, K Yu, H Zhou, X Wu, S Su - Bioengineering, 2024 - mdpi.com
Electroencephalography (EEG) is typical time-series data. Designing an automatic detection
model for EEG is of great significance for disease diagnosis. For example, EEG stands as …
model for EEG is of great significance for disease diagnosis. For example, EEG stands as …
Automated Seizure Detection using Transformer Models on Multi-Channel EEGs
Epilepsy is a prevalent neurological disorder characterized by recurring seizures, affecting
approximately 50 million individuals globally. Given the potential severity of the associated …
approximately 50 million individuals globally. Given the potential severity of the associated …
基于EEG 的癫痫自动检测: 综述与展望
彭睿旻， 江军， 匡光涛， 杜浩， 伍冬睿， 邵剑波 - 自动化学报, 2022 - aas.net.cn
癫痫是一种由脑部神经元阵发性异常超同步电活动导致的慢性非传染性疾病,
也是全球最常见的神经系统疾病之一. 基于EEG 的癫痫自动检测是指通过机器学习, 分布检验 …
也是全球最常见的神经系统疾病之一. 基于EEG 的癫痫自动检测是指通过机器学习, 分布检验 …
Meta-Learning for EEG Motor Imagery Classification
J Yu, L Duan, H Ji, J Li, Z Pang - Computing and Informatics, 2024 - cai.sk
A brain-computer interface (BCI) based on motor imagery (MI) enables users to
communicate with the computer directly using brain signals. However, due to the low signal …
communicate with the computer directly using brain signals. However, due to the low signal …