A Calibration-free Approach to Implementing P300-based Brain–computer Interface-Reference-Cited by-同舟云学术

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A Calibration-free Approach to Implementing P300-based Brain–computer Interface

Published:2022-01-24 Issue:2 Volume:14 Page:887-899
ISSN:1866-9956
Container-title:Cognitive Computation
language:en
Short-container-title:Cogn Comput

Author:

Huang Zhihua^ORCID,Guo Jiannan,Zheng Wenming,Wu Yingjie,Lin Zhixiong,Zheng Huiru

Funder

transformation program of scientific and technological achievements of jiangsu provence

natural science foundation of fujian province

Publisher

Springer Science and Business Media LLC

Subject

Cognitive Neuroscience,Computer Science Applications,Computer Vision and Pattern Recognition

Link

https://link.springer.com/content/pdf/10.1007/s12559-021-09971-1.pdf

Reference41 articles.

1. Wolpaw JR, Wolpaw EW, editors. Brain-Computer Interfaces: Principles and Practice. New York, USA: Oxford University Press; 2012.

2. Edelman BJ, Meng J, Suma D, Zurn C, Nagarajan E, Baxter BS, Cline CC, He B. Noninvasive neuroimaging enhances continuous neural tracking for device control. Sci Robot. 2019;4(31):eaaw6844.

3. Shukla PK, Chaurasiya RK, Verma S. Performance improvement of P300-based home appliances control classification using convolution neural network. Biomed Signal Process Control. 2021;63:102220.

4. Bauer R, Gharabaghi A. Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation. Front Neurosci. 2015;9: Article 36.

5. Abiri R, Borhani S, Sellers EW, Jiang Y, Zhao X. A comprehensive review of EEG-based brain-computer interface paradigms. J Neural Eng. 2019;16(1):011001.

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