Yazar "Huang, Xiuyu" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim
    Öğe
    Center transfer for supervised domain adaptation
    (Springer, 2023) Huang, Xiuyu; Zhou, Nan; Huang, Jian; Zhang, Huaidong; Pedrycz, Witold; Choi, Kup-Sze
    Domain adaptation (DA) is a popular strategy for pattern recognition and classification tasks. It leverages a large amount of data from the source domain to help train the model applied in the target domain. Supervised domain adaptation (SDA) approaches are desirable when only few labeled samples from the target domain are available. They can be easily adopted in many real-world applications where data collection is expensive. In this study, we propose a new supervision signal, namely center transfer loss (CTL), to efficiently align features under the SDA setting in the deep learning (DL) field. Unlike most previous SDA methods that rely on pairing up training samples, the proposed loss is trainable only using one-stream input based on the mini-batch strategy. The CTL exhibits two main functionalities in training to increase the performance of DL models, i.e., domain alignment and increasing the feature's discriminative power. The hyper-parameter to balance these two functionalities is waived in CTL, which is the second improvement from the previous approaches. Extensive experiments completed on well-known public datasets show that the proposed method performs better than recent state-of-the-art approaches.
  • Küçük Resim Yok
    Öğe
    Relation Learning Using Temporal Episodes for Motor Imagery Brain-Computer Interfaces
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Huang, Xiuyu; Liang, Shuang; Zhang, Yuanpeng; Zhou, Nan; Pedrycz, Witold; Choi, Kup-Sze
    For practical motor imagery (MI) brain-computer interface (BCI) applications, generating a reliable model for a target subject with few MI trials is important since the data collection process is labour-intensive and expensive. In this paper, we address this issue by proposing a few-shot learning method called temporal episode relation learning (TERL). TERL models MI with only limited trials from the target subject by the ability to compare MI trials through episode-based training. It can be directly applied to a new user without being re-trained, which is vital to improve user experience and realize real-world MIBCI applications. We develop a new and effective approach where, unlike the original episode learning, the temporal pattern between trials in each episode is encoded during the learning to boost the classification performance. We also perform an online evaluation simulation, in addition to the offline analysis that the previous studies only conduct, to better understand the performance of different approaches in real-world scenario. Extensive experiments are completed on four publicly available MIBCI datasets to evaluate the proposed TERL. Results show that TERL outperforms baseline and recent state-of-the-art methods, demonstrating competitive performance for subject-specific MIBCI where few trials are available from a target subject and a considerable number of trials from other source subjects.
  • Küçük Resim Yok
    Öğe
    Shallow Inception Domain Adaptation Network for EEG-Based Motor Imagery Classification
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Huang, Xiuyu; Choi, Kup-Sze; Zhou, Nan; Zhang, Yuanpeng; Chen, Badong; Pedrycz, Witold
    Electroencephalography (EEG) data across multiple individuals have a high variance. Directly using the data to train a deep learning (DL) model usually degrades the performance. To address this issue, we propose a shallow Inception domain adaptation framework to extract informative deep features from data of multiple subjects for accurate motor imagery (MI) recognition. To our best knowledge, the Inception architecture in DL is combined with a domain adaptation (DA) scheme for the first time for the MI classification task. The approach contains two compact Inception blocks that decode temporal features in different scales. In addition, we jointly optimize a novel combined loss function to reduce both marginal and class conditional discrepancies caused by the multimodal structure of EEG signals. The DA-based loss enables Inception blocks to take full advantage of their learning abilities to capture discriminative patterns of MI data from multiple subjects instead of relying on the target user only. To demonstrate the effectiveness of our approach, we conduct substantial experiments on two well-known data sets, brain-computer interface competition IV-2a and competition IV-2b. Results show that our model achieves better performance than state-of-the-art strategies. The proposed model is able to extract informative features from high-variant EEG data collected from different individuals and achieves accurate MI classifications.