Name
Abstract | ||
|---|---|---|
ICA is often employed for the analysis of MEG stimulus experiments. However, the assumption of independence for evoked source signals may not be valid. We present a synthetic model for stimulus evoked MEG data which can be used for the assessment and the development of BSS methods in this context. Specifically, the signal shapes as well as the degree of signal dependency are gradually adjustable. We illustrate the use of the synthetic model by applying ICA and independent subspace analysis (ISA) to data generated by this model. For evoked MEG data, we show that ICA may fail and that even results that appear physiologically meaningful, can turn out to be wrong. Our results further suggest that ISA via grouping ICA results is a promising approach to identify subspaces of dependent MEG source signals. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-00599-2_56 | ICA |
Keywords | Field | DocType |
signal dependency,bss method,evoked meg signals,dependent meg source signal,synthetic model,grouping ica result,non-independent bss,evoked source signal,evoked meg data,independent subspace analysis,meg data,meg stimulus experiment,controllable dependencies | Subspace topology,Computer science,Speech recognition,Linear subspace,Independent component analysis,Mutual information,Stimulus (physiology) | Conference |
Volume | ISSN | Citations |
5441 | 0302-9743 | 5 |
PageRank | References | Authors |
0.48 | 8 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Florian Kohl | 1 | 5 | 0.82 |
| G. Wubbeler | 2 | 125 | 15.16 |
| Dorothea Kolossa | 3 | 154 | 31.12 |
| Clemens Elster | 4 | 96 | 14.27 |
| Markus Bär | 5 | 5 | 0.82 |
| Reinhold Orglmeister | 6 | 172 | 24.04 |
| Tulay Adali | 7 | 211 | 16.65 |