DocumentCode
3461684
Title
Feature extraction of EEG based on data reduction
Author
Mu, Zhendong ; Ping Wang
Author_Institution
Inst. of Inf. & Technol., Jiangxi BlueSky Univ., Nanchang, China
Volume
3
fYear
2010
fDate
12-13 June 2010
Firstpage
275
Lastpage
277
Abstract
An important factor affecting the rate of BCI is the number of EEG features. To reduce the number of features is an important way to improve the speed. In this paper, a method of data reduction be described, features marked be used to discrete the continuous EEG, and then choose the features from the discrete data with the help of this method. The results show that classification accuracy has not been reduced but the number of features is reduction.
Keywords
brain-computer interfaces; data reduction; electroencephalography; feature extraction; medical signal processing; pattern classification; BCI; EEG; brain-computer interface; classification accuracy; data reduction; feature extraction; Accuracy; Artificial neural networks; Brain modeling; Computational modeling; Nose; Brain computer interface (BCI); data reduction; feature extraction;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer and Communication Technologies in Agriculture Engineering (CCTAE), 2010 International Conference On
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6944-4
Type
conf
DOI
10.1109/CCTAE.2010.5543396
Filename
5543396
Link To Document