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Your search keyword '"Li, Yuanqing"' showing total 26 results
Start Over Author "Li, Yuanqing" Publication Type Books
26 results on '"Li, Yuanqing"'

8. List of Contributors

Author

Aly-Hassan, Mohamed S., primary, Altstädt, Volker, additional, Asp, Leif E., additional, Baltopoulos, Athanasios, additional, Bhattacharyya, Debes, additional, Bijwe, Jayashree, additional, Botelho, Edson Cocchieri, additional, Breuer, U.P., additional, Carotenuto, G., additional, Chandrasekaran, S., additional, Chen, Yuming, additional, de Oliveira Barra, Guilherme Mariz, additional, de Sousa, Daniel Eurico Salvador, additional, Doshi, Sagar M., additional, Dubrovsky, V.V., additional, Fathi, Amir, additional, Fiedler, B., additional, Friedrich, K., additional, Frontini, Patricia M., additional, Gao, Shang-Lin, additional, Ghasemi Nejhad, Mehrdad N., additional, Greenhalgh, Emile S., additional, Gurka, Martin, additional, Huang, Haitao, additional, Ishak, Z.A. Mohd, additional, Jancar, Josef, additional, Karger-Kocsis, J., additional, Kéki, S., additional, Khun, Nay Win, additional, Kim, Nam Kyeun, additional, Kostopoulos, Vassilis, additional, Li, Xiaoyan, additional, Li, Yuanqing, additional, Liao, Kin, additional, Lucas, Alessandra de Almeida, additional, Mäder, Edith, additional, Mai, Yiu-Wing, additional, Meo, Michele, additional, Nicolais, L., additional, Nohara, Evandro Luís, additional, Pesetskii, S.S., additional, Ponomarenko, Anatoliy T., additional, Pouzada, António S., additional, Ray, Suprakas Sinha, additional, Rezende, Mirabel Cerqueira, additional, Rong, Min Zhi, additional, Schmeer, S., additional, Schulte, K., additional, Scuracchio, Carlos Henrique, additional, Shevchenko, V.V., additional, Shevchenko, Vitaliy G., additional, Subasinghe, Aruna, additional, Sue, Hung-Jue, additional, Sun, Dawei, additional, Sun, Dazhi, additional, Taib, R. Mat, additional, Thostenson, Erik T., additional, Umer, Rehan, additional, Viets, Chr., additional, Yang, Jinglei, additional, Yi, Xiao-Su, additional, Yin, Tao, additional, Yuan, Yan Chao, additional, Zhang, He, additional, Zhang, Hui, additional, Zhang, Ming Qiu, additional, Zhang, Zhong, additional, Zhou, Limin, additional, and Zhou, Ling-yun, additional

Published
2015
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21. Channel Selection for Motor Imagery-Based BCIs: A Semi-supervised SVM Algorithm.

Author

Long, Jinyi, Li, Yuanqing, and Gu, Zhenghui

Abstract

Given a frequency band, we propose a semi-supervised SVM algorithm to select a set of channels for motor imagery-based BCIs in this paper. Both training data with labels and test data without labels are used for training a classifier in this approach. Hence, it is suitable for the case of small training data set. To test our algorithm, it is applied to a BCI competition data set. Data analysis results demonstrate the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

Published
2011
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22. Classifying Motor Imagery EEG Signals by Iterative Channel Elimination according to Compound Weight.

Author

He, Lin, Gu, Zhenghui, Li, Yuanqing, and Yu, Zhuliang

Abstract

There often exist redundant channels in EEG signal collection which deteriorate the classification accuracy. In this paper, a classification method which can deal with redundant channels, as well as redundant CSP features, is presented for motor imagery task. Our method utilizes CSP filter and margin maximization with linear programming to update a compound weight that enables iterative channel elimination and the update of the following linear classification. Theoretical analysis and experimental results show the effectiveness of our method to solve redundancy of channels and CSP features simultaneously when classifying motor imagery EEG data. [ABSTRACT FROM AUTHOR]

Published
2010
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23. Digital Signal Processing and Machine Learning.

Author

Li, Yuanqing, Ang, Kai Keng, and Guan, Cuntai

Abstract

Any brain–computer interface (BCI) system must translate signals from the users brain into messages or commands (see Fig. 1). Many signal processing and machine learning techniques have been developed for this signal translation, and this chapter reviews the most common ones. Although these techniques are often illustrated using electroencephalography (EEG) signals in this chapter, they are also suitable for other brain signals. [ABSTRACT FROM AUTHOR]

Published
2010
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24. A Sparse Infrastructure of Wavelet Network for Nonparametric Regression.

Author

Zhang, Jun, Gu, Zhenghui, Li, Yuanqing, and Gao, Xieping

Abstract

In this paper, we propose a novel 4-layer infrastructure of wavelet network. It differs from the commonly used 3-layer wavelet networks in adaptive selection of wavelet neurons based on the input information. As a result, it not only alleviates widespread structural redundancy, but can also control the scale of problem solution to a certain extent. Based on this architecture, we build a new type of wavelet network for function learning. The experimental results demonstrate that our model is remarkably superior to two well-established 3-layer wavelet networks in terms of both speed and accuracy. Another comparison to Huang΄s real-time neural network shows that, at similar speed, our model achieves improvement in generalization performance. abstract environment. [ABSTRACT FROM AUTHOR]

Published
2010
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25. Analysis of Source Sparsity and Recoverability for SCA Based Blind Source Separation.

Author

Li, Yuanqing, Cichocki, Andrzej, Amari, Shun-ichi, Guan, Cuntai, Rosca, Justinian, Erdogmus, Deniz, Príncipe, José C., and Haykin, Simon

Abstract

One (of) important application of sparse component analysis (SCA) is in underdetermined blind source separation (BSS). Within a probability framework, this paper focuses on recoverability problem of underdetermined BSS based on a two-stage SCA approach. We consider a general case in which both sources and mixing matrix are randomly drawn. First, we present a recoverability probability estimate under the condition that the nonzero entry number of a source column vector is fixed. Next, we define the sparsity degree of a signal, and establish the relationship between the sparsity degree of sources and recoverability probability. Finally, we explain how to use the relationship to guarantee the performance of BSS. Several simulation results have demonstrated the validity of the probability estimation approach. [ABSTRACT FROM AUTHOR]

Published
2006
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26. ICA and Committee Machine-Based Algorithm for Cursor Control in a BCI System.

Author

Wang, Jun, Liao, Xiaofeng, Yi, Zhang, Qin, Jianzhao, Li, Yuanqing, and Cichocki, Andrzej

Abstract

In recent years, brain-computer interface (BCI) technology has emerged very rapidly. Brain-computer interfaces (BCIs) bring us a new communication interface technology which can translate brain activities into control signals of devices like computers, robots. The preprocessing of electroencephalographic (EEG) signal and translation algorithms play an important role in EEG-based BCIs. In this study, we employed an independent component analysis (ICA)-based preprocessing method and a committee machine-based translation algorithm for the offline analysis of a cursor control experiment. The results show that ICA is an efficient preprocessing method and the committee machine is a good choice for translation algorithm. [ABSTRACT FROM AUTHOR]

Published
2005
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