Dongrui Wu
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Explore the profile of Dongrui Wu including associated specialties, affiliations and a list of published articles.
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Articles
47
Citations
353
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Recent Articles
1.
An J, Peng R, Du Z, Liu H, Hu F, Shu K, et al.
J Neural Eng
. 2025 Feb;
22(2).
PMID: 39993329
. Epilepsy is a neurological disorder that affects millions of patients worldwide. Electroencephalogram-based seizure detection plays a crucial role in its timely diagnosis and effective monitoring. However, due to distribution...
2.
Chen X, Meng L, Xu Y, Wu D
J Neural Eng
. 2024 Oct;
21(5).
PMID: 39433071
. machine learning has achieved significant success in electroencephalogram (EEG) based brain-computer interfaces (BCIs), with most existing research focusing on improving the decoding accuracy. However, recent studies have shown that...
3.
Chen X, Li S, Tu Y, Wang Z, Wu D
J Neural Eng
. 2024 Oct;
22(1).
PMID: 39423826
. An electroencephalogram (EEG)-based brain-computer interface (BCI) is a direct communication pathway between the human brain and a computer. Most research so far studied more accurate BCIs, but much less...
4.
Jia T, Meng L, Li S, Liu J, Wu D
IEEE Trans Neural Syst Rehabil Eng
. 2024 Sep;
32:3442-3451.
PMID: 39255189
Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical consideration. Federated learning (FL)...
5.
Wang Z, Li S, Luo J, Liu J, Wu D
Neural Netw
. 2024 May;
176:106351.
PMID: 38713969
A brain-computer interface (BCI) enables direct communication between the human brain and external devices. Electroencephalography (EEG) based BCIs are currently the most popular for able-bodied users. To increase user-friendliness, usually...
6.
Chen X, Wang Z, Wu D
IEEE Trans Neural Syst Rehabil Eng
. 2024 Apr;
32:1703-1714.
PMID: 38648154
Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI studies focused on improving the decoding accuracy, with only a few considering the adversarial...
7.
Peng R, Du Z, Zhao C, Luo J, Liu W, Chen X, et al.
IEEE Trans Neural Syst Rehabil Eng
. 2024 Feb;
32:831-839.
PMID: 38349833
Cross-subject electroencephalogram (EEG) based seizure subtype classification is very important in precise epilepsy diagnostics. Deep learning is a promising solution, due to its ability to automatically extract latent patterns. However,...
8.
Wu H, Li S, Wu D
IEEE Trans Neural Syst Rehabil Eng
. 2024 Jan;
32:527-536.
PMID: 38252572
Motor imagery (MI) based brain-computer interfaces (BCIs) enable the direct control of external devices through the imagined movements of various body parts. Unlike previous systems that used fixed-length EEG trials...
9.
Du Z, Peng R, Liu W, Li W, Wu D
IEEE Trans Neural Syst Rehabil Eng
. 2023 Nov;
31:4781-4789.
PMID: 38032784
Epilepsy is a pervasive neurological disorder affecting approximately 50 million individuals worldwide. Electroencephalogram (EEG) based seizure subtype classification plays a crucial role in epilepsy diagnosis and treatment. However, automatic seizure...
10.
Jiang X, Meng L, Wang Z, Wu D
IEEE Trans Biomed Eng
. 2023 Nov;
71(4):1308-1318.
PMID: 37971908
Objective: An electroencephalogram (EEG) based brain-computer interface (BCI) maps the user's EEG signals into commands for external device control. Usually a large amount of labeled EEG trials are required to...