Attention Optimization Method for EEG via the TGAM
Joint Authors
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-18
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Since the 21st century, noninvasive brain-computer interface (BCI) has developed rapidly, and brain-computer devices have gradually moved from the laboratory to the mass market.
Among them, the TGAM (ThinkGear Asic Module) and its encapsulate algorithm have been adopted by many research teams and faculty members around the world.
However, due to the limited development cost, the effectiveness of the algorithm to calculate data is not satisfactory.
This paper proposes an attention optimization algorithm based on the TGAM for EEG data feedback.
Considering that the data output of the TGAM encapsulate algorithm fluctuates greatly, the delay is high and the accuracy is low.
The experimental results demonstrated that our algorithm can optimize EEG data, so that with the same or even lower delay and without changing the encapsulate algorithm of the module itself, it can significantly improve the performance of attention data, greatly improve the stability and accuracy of data, and achieve better results in practical applications.
American Psychological Association (APA)
Wu, Yu& Xie, Ning. 2020. Attention Optimization Method for EEG via the TGAM. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1139519
Modern Language Association (MLA)
Wu, Yu& Xie, Ning. Attention Optimization Method for EEG via the TGAM. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1139519
American Medical Association (AMA)
Wu, Yu& Xie, Ning. Attention Optimization Method for EEG via the TGAM. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1139519
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139519