Zobrazeno 1 - 10
of 508
pro vyhledávání: '"Wu, Dongrui"'
CSP-Net: Common Spatial Pattern Empowered Neural Networks for EEG-Based Motor Imagery Classification
Publikováno v:
Knowledge Based Systems, 305:112668, 2024
Electroencephalogram-based motor imagery (MI) classification is an important paradigm of non-invasive brain-computer interfaces. Common spatial pattern (CSP), which exploits different energy distributions on the scalp while performing different MI ta
Externí odkaz:
http://arxiv.org/abs/2411.11879
Objective: 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 attention has been paid to the ethi
Externí odkaz:
http://arxiv.org/abs/2411.10469
Publikováno v:
IEEE Trans. on Neural Systems and Rehabilitation Engineering, 32:1703-1714, 2024
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 security. Although man
Externí odkaz:
http://arxiv.org/abs/2411.02094
Open Set Domain Adaptation (OSDA) aims to cope with the distribution and label shifts between the source and target domains simultaneously, performing accurate classification for known classes while identifying unknown class samples in the target dom
Externí odkaz:
http://arxiv.org/abs/2311.00285
Random label noises (or observational noises) widely exist in practical machine learning settings. While previous studies primarily focus on the affects of label noises to the performance of learning, our work intends to investigate the implicit regu
Externí odkaz:
http://arxiv.org/abs/2304.00320
Autor:
Chen, Xiaoqing, Wu, Dongrui
Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI research focused on improving its accuracy, but few had considered its security. Recent studies, however, have shown t
Externí odkaz:
http://arxiv.org/abs/2212.00727
Facial affect analysis remains a challenging task with its setting transitioned from lab-controlled to in-the-wild situations. In this paper, we present novel frameworks to handle the two challenges in the 4th Affective Behavior Analysis In-The-Wild
Externí odkaz:
http://arxiv.org/abs/2207.09748
This paper presents PyTSK, a Python toolbox for developing Takagi-Sugeno-Kang (TSK) fuzzy systems. Based on scikit-learn and PyTorch, PyTSK allows users to optimize TSK fuzzy systems using fuzzy clustering or mini-batch gradient descent (MBGD) based
Externí odkaz:
http://arxiv.org/abs/2206.03310
Publikováno v:
In Neurocomputing 7 December 2024 609
Principal component analysis (PCA) has been widely used as an effective technique for feature extraction and dimension reduction. In the High Dimension Low Sample Size (HDLSS) setting, one may prefer modified principal components, with penalized load
Externí odkaz:
http://arxiv.org/abs/2110.03273