| Zugriffsnummer | 27846 |
| Dokumenttyp | Konferenzartikel |
| Sprache | Englisch |
| Titel | Noise adjusted PCA for finding the subspace of evoked dependent signals from MEG data |
| Autor(in); Institution |
Kohl, Florian; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin
Wübbeler, Gerd; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin
Kolossa, Dorothea; TU, Berlin, GERMANY
Elster, Clemens; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin
Bär, Markus; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin
Orglmeister, Reinhold; TU, Berlin, GERMANY
|
| Quelle/Jahr | Latent variable analysis and signal separation: 9th international conference, LVA/ICA 2010, St. Malo, France, September 27-30, 2010 ; proceedings:(2010), 442 - 449 |
| Schriftenreihe | Lecture Notes in Computer Science: 6365 |
| ISSN | 0302-9743 |
| ISBN | 978-3-642-15994-7 |
| Verlag | Berlin [u.a.]: Springer |
| Konferenzangaben | 9th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA), St. Malo, 27-30, September, 2010 |
| Freie Schlagworte | Magnetoencephalography ; Independent component analysis ; Noise adjusted principal component analysis |
| Zusammenfassung | Evoked signals that underlie multi-channel magnetoencephalography (MEG) data can be dependent. It follows that ICA can fail to separate the evoked dependent signals. As a first step towards separation, we adress the problem of finding a subspace of possibly mixed evoked signals that are separated from the non-evoked signals. Specifically, a vector basis of the evoked subspace and the associated mixed signals are of interest. It was conjectured that ICA followed by clustering is suitable for this subspace analysis. As an alternative, we propose the use of noise adjusted PCA (NAPCA). Thismethod uses two covariancematrices obtained frompre- and post-stimulation data in order to find a subspace basis. Subsequently, the associated signals are obtained by linear projection onto the estimated basis. Synthetic and recorded data are analyzed and the performance of NAPCA and the ICA approach is compared. Our results suggest that ICA followed by clustering is a valid approach. Nevertheless, NAPCA outperforms the ICA approach for synthetic and for real MEG data from a study with simultaneous visual and auditory stimulation. Hence, NAPCA should be considered as a viable alternative for the analysis of evoked MEG data. |
Zitierung
Kohl, F., Wübbeler, G., Kolossa, D., Elster, C., Bär, M., & Orglmeister, R. (2010). Noise adjusted PCA for finding the subspace of evoked dependent signals from MEG data. 9th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA), St. Malo, 27-30, September, 2010.