Fast feature selection algorithm of EEG data based on GPU technology
by Bin Liu; Han Deng; Tianke Fang; Meixuan Chen
International Journal of Embedded Systems (IJES), Vol. 14, No. 6, 2021

Abstract: With the rapid development of EEG technology, the rapid selection of EEG data features makes it possible for subsequent applications. To improve the relevance of EEG data, this paper proposes a feature selection algorithm of EEG data based on GPU technology. This method takes the whole gene pathway as a whole, takes its EEG data path as a specific variable, and quantifies each path by distance measurement; secondly, it uses regularisation dimension reduction technology to brush out the essential features of EEG data and makes use of this method to select the essential features of EEG data. Finally, principal component analysis (PCA) is used to evaluate the path model quickly and accurately. The experimental analysis shows that the method solves the problems of high requirement of EEG data sample distribution and slow evaluation speed. Moreover, the method proposed in this paper shows the great ability of real-time performance.

Online publication date: Thu, 24-Feb-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Embedded Systems (IJES):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com