Výsledky vyhledávání - "EEG decoding algorithm"

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  1. 1

    SMMTM: Motor imagery EEG decoding algorithm using a hybrid multi-branch separable convolutional self-attention temporal convolutional network Autor Cao, DianGuo, Yu, ZhenYuan, Wang, Jinqiang, Wu, Yuqiang

    ISSN: 1932-6203, 1932-6203
    Vydáno: United States Public Library of Science 23.10.2025
    Vydáno v PloS one (23.10.2025)
    “…Motor imagery (MI) is a brain-computer interface (BCI) technology with the potential to change human life in the future. MI signals have been widely applied in…”
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    Journal Article
  2. 2

    SMMTM: Motor imagery EEG decoding algorithm using a hybrid multi-branch separable convolutional self-attention temporal convolutional network Autor DianGuo Cao, ZhenYuan Yu, Jinqiang Wang, Yuqiang Wu

    ISSN: 1932-6203
    Vydáno: Public Library of Science (PLoS) 01.01.2025
    Vydáno v PloS one (01.01.2025)
    “…Motor imagery (MI) is a brain-computer interface (BCI) technology with the potential to change human life in the future. MI signals have been widely applied in…”
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    Journal Article
  3. 3

    Combining multi-scale convolutional neural network and Transformers for EEG-Based RSVP detection Autor Lu, Gai, Zhang, Yifan, Chu, Xingxing, Liu, Yingxin, Yu, Yang

    Vydáno: IEEE 19.11.2022
    “… It is necessary to develop an EEG decoding algorithm with robust generalization ability and high recognition accuracy…”
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  4. 4

    A classification algorithm of an SSVEP brain-Computer interface based on CCA fusion wavelet coefficients Autor Ma, Pengfei, Dong, Chaoyi, Lin, Ruijing, Ma, Shuang, Jia, Tingting, Chen, Xiaoyan, Xiao, Zhiyun, Qi, Yongsheng

    ISSN: 0165-0270, 1872-678X, 1872-678X
    Vydáno: Netherlands Elsevier B.V 01.04.2022
    Vydáno v Journal of neuroscience methods (01.04.2022)
    “…In the study of brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs), how to improve the classification accuracies of BCIs…”
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    Journal Article
  5. 5

    Boosting-LDA algriothm with multi-domain feature fusion for motor imagery EEG decoding Autor Zhang, Yue, Chen, Weihai, Lin, Chun-Liang, Pei, Zhongcai, Chen, Jianer, Chen, Zuobing

    ISSN: 1746-8094, 1746-8108
    Vydáno: Elsevier Ltd 01.09.2021
    “… Understanding EEG differences between healthy people and stroke patients is important. A Novel EEG decoding algorithm is proposed based on this present situation…”
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    Journal Article
  6. 6

    Brain–Machine Interface and Visual Compressive Sensing-Based Teleoperation Control of an Exoskeleton Robot Autor Qiu, Shiyuan, Li, Zhijun, He, Wei, Zhang, Longbin, Yang, Chenguang, Su, Chun-Yi

    ISSN: 1063-6706, 1941-0034
    Vydáno: IEEE 01.02.2017
    Vydáno v IEEE transactions on fuzzy systems (01.02.2017)
    “…This paper presents a teleoperation control for an exoskeleton robotic system based on the brain-machine interface and vision feedback. Vision compressive…”
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    Journal Article
  7. 7
  8. 8

    EEG-ReMinD: Enhancing Neurodegenerative EEG Decoding through Self-Supervised State Reconstruction-Primed Riemannian Dynamics Autor Wang, Zirui, Song, Zhenxi, Guo, Yi, Liu, Yuxin, Xu, Guoyang, Zhang, Min, Zhang, Zhiguo

    ISSN: 2379-190X
    Vydáno: IEEE 06.04.2025
    “…The development of EEG decoding algorithms confronts challenges such as data sparsity, subject variability, and the need for precise annotations, all of which are vital for advancing brain-computer…”
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  9. 9

    Learning Signal Representations for EEG Cross-Subject Channel Selection and Trial Classification Autor Massi, Michela C., Ieva, Francesca

    Vydáno: IEEE 25.10.2021
    “… Most EEG systems are multi-channel in nature, but multiple channels might include noisy and redundant information and increase computational times of automated EEG decoding algorithms…”
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