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
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;
Conference_Titel :
Computer and Communication Technologies in Agriculture Engineering (CCTAE), 2010 International Conference On
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-6944-4
DOI :
10.1109/CCTAE.2010.5543396
