.png){kind=icon}
返回
Optimizing spatial filters for robust EEG single-trial analysis
.png){kind=icon}
PRE
AI
DOI：10.1109/MSP.2008.4408441.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Due to the volume conduction multichannel electroencephalogram (EEG) recordings give a rather blurred image of brain activity. Therefore spatial filters are extremely useful in single-trial analysis in order to improve the signal-to-noise ratio. There are powerful methods from machine learning and signal processing that permit the optimization of spatio-temporal filters for each subject in a data dependent fashion beyond the fixed filters based on the sensor geometry, e.g., Laplacians. Here we elucidate the theoretical background of the common spatial pattern (CSP) algorithm, a popular method in brain-computer interface (BCI) research. Apart from reviewing several variants of the basic algorithm, we reveal tricks of the trade for achieving a powerful CSP performance, briefly elaborate on theoretical aspects of CSP, and demonstrate the application of CSP-type preprocessing in our studies of the Berlin BCI (BBCI) project.
Keyword:
BRAIN-COMPUTER INTERFACE
MOTOR IMAGERY
CLASSIFICATION
COMMUNICATION
EXTRACTION
DYNAMICS
RHYTHMS
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
机构
暂无机构信息
引用论文
Evaluation of an acoustic plane wave transmitted through a rectangular hole in a thick hard screen.png)
Wave Motion
Wave MotionIF0
PREAI
分享收藏
查看更多内容