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  • 标题:Advances in Hybrid Brain-Computer Interfaces: Principles, Design, and Applications
  • 本地全文:下载
  • 作者:Zina Li ; Shuqing Zhang ; Jiahui Pan
  • 期刊名称:Computational Intelligence and Neuroscience
  • 印刷版ISSN:1687-5265
  • 电子版ISSN:1687-5273
  • 出版年度:2019
  • 卷号:2019
  • 页码:1-10
  • DOI:10.1155/2019/3807670
  • 出版社:Hindawi Publishing Corporation
  • 摘要:Conventional brain-computer interface (BCI) systems have been facing two fundamental challenges: the lack of high detection performance and the control command problem. To this end, the researchers have proposed a hybrid brain-computer interface (hBCI) to address these challenges. This paper mainly discusses the research progress of hBCI and reviews three types of hBCI, namely, hBCI based on multiple brain models, multisensory hBCI, and hBCI based on multimodal signals. By analyzing the general principles, paradigm designs, experimental results, advantages, and applications of the latest hBCI system, we found that using hBCI technology can improve the detection performance of BCI and achieve multidegree/multifunctional control, which is significantly superior to single-mode BCIs.
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