Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Robust Common Spatial Filters with a Maxmin Approach

Authors
Kawanabe, MotoakiSamek, WojciechMueller, Klaus-RobertVidaurre, Carmen
Issue Date
2월-2014
Publisher
MIT PRESS
Citation
NEURAL COMPUTATION, v.26, no.2, pp.349 - 376
Indexed
SCIE
SCOPUS
Journal Title
NEURAL COMPUTATION
Volume
26
Number
2
Start Page
349
End Page
376
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/99363
DOI
10.1162/NECO_a_00544
ISSN
0899-7667
Abstract
Electroencephalographic signals are known to be nonstationary and easily affected by artifacts; therefore, their analysis requires methods that can deal with noise. In this work, we present a way to robustify the popular common spatial patterns (CSP) algorithm under a maxmin approach. In contrast to standard CSP that maximizes the variance ratio between two conditions based on a single estimate of the class covariance matrices, we propose to robustly compute spatial filters by maximizing the minimum variance ratio within a prefixed set of covariance matrices called the tolerance set. We show that this kind of maxmin optimization makes CSP robust to outliers and reduces its tendency to overfit. We also present a data-driven approach to construct a tolerance set that captures the variability of the covariance matrices over time and shows its ability to reduce the nonstationarity of the extracted features and significantly improve classification accuracy. We test the spatial filters derived with this approach and compare them to standard CSP and a state-of-the-art method on a real-world brain-computer interface (BCI) data set in which we expect substantial fluctuations caused by environmental differences. Finally we investigate the advantages and limitations of the maxmin approach with simulations.
Files in This Item
There are no files associated with this item.
Appears in
Collections
Graduate School > Department of Artificial Intelligence > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetrics

Total Views & Downloads

BROWSE