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Yangyang Miao
Researcher at East China University of Science and Technology
Publications - 21
Citations - 873
Yangyang Miao is an academic researcher from East China University of Science and Technology. The author has contributed to research in topics: Computer science & Feature extraction. The author has an hindex of 8, co-authored 21 publications receiving 339 citations. Previous affiliations of Yangyang Miao include China University of Science and Technology.
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Journal ArticleDOI
Correlation-based channel selection and regularized feature optimization for MI-based BCI
TL;DR: A correlation-based channel selection (CCS) method is proposed to select the channels that contained more correlated information in this study to improve the classification performance of MI-based BCIs and a novel regularized common spatial pattern (RCSP) method was used to extract effective features.
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Internal Feature Selection Method of CSP Based on L1-Norm and Dempster–Shafer Theory
TL;DR: A new feature selection method based on the Dempster–Shafer theory is proposed, which takes into consideration the distribution of features and results in a significant increase in the performance of MI-based BCI systems.
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The Study of Generic Model Set for Reducing Calibration Time in P300-Based Brain–Computer Interface
TL;DR: The strategy of combining the generic models with online training is easily accepted and achieves higher levels of user satisfaction (as measured by subjective reports), which provides a valuable new strategy for improving the performance of P300-based BCI.
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Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing
TL;DR: Compared to the other state-of-the-art methods, the proposed bispectrum-based channel selection (BCS) method can achieve significantly better classification accuracies for MI-based BCI (Wilcoxon signed test, p < 0.05).
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Developing a Novel Tactile P300 Brain-Computer Interface With a Cheeks-Stim Paradigm
TL;DR: A novel tactile P300 BCI paradigm is proposed for further expanding the tactile stimulation methods and might lead to many promising applications of such BCIs.