Abstract
Electroencephalographic signals are known to be non-stationary and easily affected by artifacts, therefore their analysis requires methods that can deal with noise. In this work we present two ways of calculating robust common spatial patterns under a maxmin approach. The worst-case objective function is optimized within prefixed sets of the covariance matrices that are defined either very simply as identity matrices or in a data driven way using PCA. We test common spatial filters derived with these two approaches with real world brain-computer interface (BCI) data sets in which we expect substantial "day-to-day" fluctuations (session transfer problem). We compare our results with the classical common spatial filters and show that both can improve the performance of the latter.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 |
| Pages | 2470-2473 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 2009 Dec 1 |
| Externally published | Yes |
| Event | 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 - Minneapolis, MN, United States Duration: 2009 Sep 2 → 2009 Sep 6 |
Other
| Other | 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 |
|---|---|
| Country | United States |
| City | Minneapolis, MN |
| Period | 09/9/2 → 09/9/6 |
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ASJC Scopus subject areas
- Cell Biology
- Developmental Biology
- Biomedical Engineering
- Medicine(all)
Cite this
Robust common spatial filters with a maxmin approach. / Kawanabe, Motoaki; Vidaurre, Carmen; Scholler, Simon; Muller, Klaus.
Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009. 2009. p. 2470-2473 5334786.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Robust common spatial filters with a maxmin approach
AU - Kawanabe, Motoaki
AU - Vidaurre, Carmen
AU - Scholler, Simon
AU - Muller, Klaus
PY - 2009/12/1
Y1 - 2009/12/1
N2 - Electroencephalographic signals are known to be non-stationary and easily affected by artifacts, therefore their analysis requires methods that can deal with noise. In this work we present two ways of calculating robust common spatial patterns under a maxmin approach. The worst-case objective function is optimized within prefixed sets of the covariance matrices that are defined either very simply as identity matrices or in a data driven way using PCA. We test common spatial filters derived with these two approaches with real world brain-computer interface (BCI) data sets in which we expect substantial "day-to-day" fluctuations (session transfer problem). We compare our results with the classical common spatial filters and show that both can improve the performance of the latter.
AB - Electroencephalographic signals are known to be non-stationary and easily affected by artifacts, therefore their analysis requires methods that can deal with noise. In this work we present two ways of calculating robust common spatial patterns under a maxmin approach. The worst-case objective function is optimized within prefixed sets of the covariance matrices that are defined either very simply as identity matrices or in a data driven way using PCA. We test common spatial filters derived with these two approaches with real world brain-computer interface (BCI) data sets in which we expect substantial "day-to-day" fluctuations (session transfer problem). We compare our results with the classical common spatial filters and show that both can improve the performance of the latter.
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UR - http://www.scopus.com/inward/citedby.url?scp=77950963236&partnerID=8YFLogxK
U2 - 10.1109/IEMBS.2009.5334786
DO - 10.1109/IEMBS.2009.5334786
M3 - Conference contribution
C2 - 19964963
AN - SCOPUS:77950963236
SN - 9781424432967
SP - 2470
EP - 2473
BT - Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009
ER -