Paper Info
Title
Importance weighted extreme energy ratio for EEG classification
Abstract
Spatial filtering is important for EEG signal processing since raw scalp EEG potentials have a poor spatial resolution due to the volume conduction effect. Extreme energy ratio (EER) is a recently proposed feature extractor which exhibits good performance. However, the performance of EER will be degraded by some factors such as outliers and the time-variances between the training and test sessions. Unfortunately, these limitations are common in the practical brain-computer interface (BCI) applications. This paper proposes a new feature extraction method called importance-weighted EER (IWEER) by defining two kinds of weight termed intra-trial importance and inter-trial importance. These weights are defined with the density ratio theory and assigned to the data points and trials respectively to improve the estimation of covariance matrices. The spatial filters learned by the IWEER are both robust to the outliers and adaptive to the test samples. Compared to the previous EER, experimental results on nine subjects demonstrate the better classification ability of the IWEER method.
Year
DOI
Venue
2010
10.1007/978-3-642-17534-3_2
ICONIP
Keywords
Field
DocType
eeg signal processing,previous eer,iweer method,eeg classification,extreme energy ratio,density ratio theory,good performance,feature extractor,spatial filter,poor spatial resolution,inter-trial importance,spatial resolution,spatial filtering,feature extraction,brain computer interface,signal processing
Data point,Pattern recognition,Computer science,Matrix (mathematics),Brain–computer interface,Outlier,Feature extraction,Artificial intelligence,Image resolution,Machine learning,Spatial filter,Covariance
Conference
Volume
ISSN
ISBN
6444
0302-9743
3-642-17533-3
Citations 
PageRank 
References 
0
0.34
3
Authors
2
Name
Order
Citations
PageRank
Wenting Tu1859.48
Shiliang Sun21732115.55