Zobrazeno 1 - 10
of 517
pro vyhledávání: '"Wu Dongrui"'
An electroencephalogram (EEG) based brain-computer interface (BCI) enables direct communication between the brain and external devices. However, EEG-based BCIs face at least three major challenges in real-world applications: data scarcity and individ
Externí odkaz:
http://arxiv.org/abs/2412.11390
Publikováno v:
IEEE Trans. on Neural Systems and Rehabilitation Engineering, 32:527-536, 2024
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 for MI decoding, asynchronous BCIs
Externí odkaz:
http://arxiv.org/abs/2412.09006
Publikováno v:
L. Meng, X. Jiang, X. Chen, W. Liu, H. Luo and D. Wu, Adversarial Filtering Based Evasion and Backdoor Attacks to EEG-Based Brain-Computer Interfaces, Information Fusion, 107:102316, 2024
A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is a common input signal for BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the
Externí odkaz:
http://arxiv.org/abs/2412.07231
Publikováno v:
S. Li, Z. Wang, H. Luo, L. Ding and D. Wu, T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs, IEEE Trans. on Biomedical Engineering, 71(2):423-432, 2024
Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, such BCIs usually require a subject-
Externí odkaz:
http://arxiv.org/abs/2412.07228
Autor:
Peng, Ruimin, Du, Zhenbang, Zhao, Changming, Luo, Jingwei, Liu, Wenzhong, Chen, Xinxing, Wu, Dongrui
Publikováno v:
IEEE Trans. on Neural Systems and Rehabilitation Engineering, 32:831-839, 2024
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, it usually r
Externí odkaz:
http://arxiv.org/abs/2412.15224
Publikováno v:
Neural Networks, 176:106351, 2024
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 a small a
Externí odkaz:
http://arxiv.org/abs/2412.03224
Publikováno v:
IEEE Trans. on Artificial Intelligence, 5(7):3431-3444, 2024
Semi-supervised domain adaptation (SSDA) aims at training a high-performance model for a target domain using few labeled target data, many unlabeled target data, and plenty of auxiliary data from a source domain. Previous works in SSDA mainly focused
Externí odkaz:
http://arxiv.org/abs/2412.03212
Emotion recognition is a critical component of affective computing. Training accurate machine learning models for emotion recognition typically requires a large amount of labeled data. Due to the subtleness and complexity of emotions, multiple evalua
Externí odkaz:
http://arxiv.org/abs/2412.01171
Publikováno v:
NeuroComputing, 609:128477, 2024
Spiking neural networks (SNNs) aim to simulate real neural networks in the human brain with biologically plausible neurons. The leaky integrate-and-fire (LIF) neuron is one of the most widely studied SNN architectures. However, it has the vanishing g
Externí odkaz:
http://arxiv.org/abs/2412.01087
Publikováno v:
IEEE Trans. on Neural Systems and Rehabilitation Engineering, 32:3442-3451, 2024
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) is a promising solution to this ch
Externí odkaz:
http://arxiv.org/abs/2412.01079