Signal to Image Conversion and Convolutional Neural Networks for Physiological Signal Processing: A Review
Physiological signals obtained from electroencephalography (EEG), electromyography
(EMG), and electrocardiography (ECG) provide valuable clinical information but pose …
(EMG), and electrocardiography (ECG) provide valuable clinical information but pose …
A Temporal Multi-view Fuzzy Classifier for Fusion Identification on Epileptic Brain Network
Z Xia, W Xue, J Zhai, T Zhou… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Brain networks are commonly used to identify cognitive neurobehavioral and brain
conscious disorders. Most of the studies on state networks focus on the characterization and …
conscious disorders. Most of the studies on state networks focus on the characterization and …
Parallel Dual-Branch Fusion Network for Epileptic Seizure Prediction
Epilepsy is a prevalent chronic disorder of the central nervous system. The timely and
accurate seizure prediction using the scalp Electroencephalography (EEG) signal can make …
accurate seizure prediction using the scalp Electroencephalography (EEG) signal can make …
Advancing EEG-based gaze prediction using depthwise separable convolution and enhanced pre-processing
In the field of EEG-based gaze prediction, the application of deep learning to interpret
complex neural data poses significant challenges. This study evaluates the effectiveness of …
complex neural data poses significant challenges. This study evaluates the effectiveness of …
[HTML][HTML] EEG-Based Seizure Prediction Using Hybrid DenseNet–ViT Network with Attention Fusion
S Yuan, K Yan, S Wang, JX Liu, J Wang - Brain Sciences, 2024 - mdpi.com
Epilepsy seizure prediction is vital for enhancing the quality of life for individuals with
epilepsy. In this study, we introduce a novel hybrid deep learning architecture, merging …
epilepsy. In this study, we introduce a novel hybrid deep learning architecture, merging …
A modification to the Kuramoto model to simulate epileptic seizures as synchronization
JA Zavaleta-Viveros, P Toledo… - Journal of Mathematical …, 2023 - Springer
The Kuramoto model was developed to describe the coupling of oscillators, motivated by the
natural synchronization phenomena. We are interested in modeling an epileptic seizure …
natural synchronization phenomena. We are interested in modeling an epileptic seizure …
神经网络算法在癫痫预测模型中的应用研究综述.
黄红红， 张丰， 吕良福， 司霄鹏 - Journal of Frontiers of …, 2023 - search.ebscohost.com
癫痫作为一种大脑神经元异常放电导致的中枢神经系统疾病, 给患者的正常生活带来了极大影响
, 提前预测癫痫发作并及时采取防范措施可以有效提高患者的生活质量. 随着数据科学和大数据 …
, 提前预测癫痫发作并及时采取防范措施可以有效提高患者的生活质量. 随着数据科学和大数据 …
基于多频带路径签名特征的癫痫脑电图信号分类方法.
郭礼华， 杨辉， 吴倩仪， 茅海峰 - Journal of South China …, 2024 - search.ebscohost.com
基于脑电图(EEG) 信号的癫痫自动检测对癫痫的临床诊断和治疗有很大的帮助.
由于大部分癫痫识别算法忽略了EEG 信号的时序关系, 为此, 文中提出了一种基于多频带路径 …
由于大部分癫痫识别算法忽略了EEG 信号的时序关系, 为此, 文中提出了一种基于多频带路径 …
[PDF][PDF] Advancing EEG-Based Gaze Prediction: Depthwise Separable Convolution and Pre-Processing Enhancements in EEGViT
ML Key, T Mehtiyev, X Qu - researchgate.net
In the field of EEG-based gaze prediction, the application of deep learning to interpret
complex neural data poses significant challenges. This study evaluates the effectiveness of …
complex neural data poses significant challenges. This study evaluates the effectiveness of …