A frequency-weighted method combined with Common Spatial Patterns for electroencephalogram classification in brain-computer interface

被引：8
|
作者：
Liu, Guangquan [1 ]
Huang, Gan [1 ]
Meng, Jianjun [1 ]
Zhu, Xiangyang [1 ]
机构：
[1] Shanghai Jiao Tong Univ, State Key Lab Mech Syst & Vibrat, Shanghai 200240, Peoples R China
来源：
BIOMEDICAL SIGNAL PROCESSING AND CONTROL | 2010年 / 5卷 / 02期
关键词：
Brain-computer interface (BCI); Electroencephalogram (EEC); Common Spatial Patterns (CSP); Frequency-weighted method (FWM); SINGLE-TRIAL EEG; INDEPENDENT COMPONENTS-ANALYSIS; MOTOR IMAGERY; SPECTRAL FILTERS; HAND MOVEMENT; EXISTENCE; RHYTHMS; MU;
D O I：
10.1016/j.bspc.2010.02.004
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
Common Spatial Patterns (CSP) has been proven to be a powerful and successful method in the detection of event-related desynchronization (ERD) and ERD based brain-computer Interface (BC!) However, frequency optimization combined with CSP has only been investigated by a few groups In this paper, a frequency-weighted method (FWM) is proposed to optimize the frequency spectrum of surface electroencephalogram (EEG) signals for a two-class mental task classification This straightforward method computes a weight value for each frequency component according to its importance for the discrimination task and reforms the spectrum with the computed weights. The off-line analysis shows that the proposed method achieves an improvement of about 4% (averaged over 24 datasets) in terms of cross-validation accuracy over the basic CSP. (C) 2010 Elsevier Ltd All rights reserved
引用
收藏
页码：174 / 180
页数：7
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