Eliminating or Shortening the Calibration for a P300 Brain–Computer Interface Based on a Convolutional Neural Network and Big Electroencephalography Data: An Online Study-Reference-Cited by-同舟云学术

Eliminating or Shortening the Calibration for a P300 Brain–Computer Interface Based on a Convolutional Neural Network and Big Electroencephalography Data: An Online Study

Published:2023 Issue: Volume:31 Page:1754-1763
ISSN:1534-4320
Container-title:IEEE Transactions on Neural Systems and Rehabilitation Engineering
language:
Short-container-title:IEEE Trans. Neural Syst. Rehabil. Eng.

Author:

Gao Wei¹^ORCID,Huang Weichen¹^ORCID,Li Man¹^ORCID,Gu Zhenghui¹^ORCID,Pan Jiahui²^ORCID,Yu Tianyou¹^ORCID,Yu Zhu Liang¹^ORCID,Li Yuanqing¹^ORCID

Affiliation:

1. School of Automation Science and Engineering, South China University of Technology, Guangzhou, China

2. School of Software, South China Normal University, Guangzhou, China

Funder

STI 2030–Major Projects

Key Realm Research and Development Program of Guangzhou, China

National Natural Science Foundation of China

Technology Innovation 2030

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering,General Neuroscience,Internal Medicine,Rehabilitation

Link

http://xplorestaging.ieee.org/ielx7/7333/10031624/10077523.pdf?arnumber=10077523

Reference43 articles.

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2. An online semi-supervised P300 speller based on extreme learning machine

3. Learning adaptive subject-independent P300 models for EEG-based brain–computer interfaces;lu;Proc IEEE Int Joint Conf Neural Netw (IEEE World Congr Comput Intelligence),2008

4. An online semi-supervised brain–computer interface;gu;IEEE Trans Biomed Eng,2013

5. MsCNN: A Deep Learning Framework for P300-Based Brain–Computer Interface Speller

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