| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0085969 |
| utime |
20240111140650.0 |
| mtime |
20070919235959.9 |
| title
(primary) (eng) |
A fast algorithm for blind separation of non-Gaussian and time-correlated signals |
| specification |
| page_count |
5 s. |
| media_type |
CD ROM |
|
| serial |
| ARLID |
cav_un_epca*0086003 |
| ISBN |
978-83-921340-2-2 |
| title
|
Proccedings of the 15th European Signal Processing Conference. EUSIPCO 2007 |
| page_num |
1731-1735 |
| publisher |
| place |
Poznan |
| name |
PTETiS |
| year |
2007 |
|
|
| title
(cze) |
Rychlý algoritmus pro slepou separaci ne-Gaussovských a časově korelovaných signálů |
| keyword |
Blind source separation |
| keyword |
multidimensional independent components |
| keyword |
FCOMBI |
| author
(primary) |
| ARLID |
cav_un_auth*0230093 |
| name1 |
Gómez-Herrero |
| name2 |
G. |
| country |
FI |
|
| author
|
| ARLID |
cav_un_auth*0108100 |
| name1 |
Koldovský |
| name2 |
Zbyněk |
| institution |
UTIA-B |
| full_dept |
Department of Stochastic Informatics |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101212 |
| name1 |
Tichavský |
| name2 |
Petr |
| institution |
UTIA-B |
| full_dept |
Department of Stochastic Informatics |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0230094 |
| name1 |
Egiazarian |
| name2 |
K. |
| country |
FI |
|
| source |
| source_type |
textový soubor |
| source_size |
367 kB |
|
| cas_special |
| project |
| project_id |
1M0572 |
| agency |
GA MŠk |
| ARLID |
cav_un_auth*0001814 |
|
| project |
| project_id |
GP102/07/P384 |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0227962 |
|
| project |
| project_id |
GA102/05/0278 |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0051169 |
|
| research |
CEZ:AV0Z10750506 |
| abstract
(eng) |
In this article we propose a computationally efficient method (termed FCOMBI) to combine the strengths of non-Gaussianity based blind source separation (BSS) and cross-correlation-based BSS. This is done by fusing the separtion abilities of two well known algorithms: EFICA and WASOBI. The algorithm is suitable for the analysis of very high-dimensional datasets like high-density Electroencephalogram or Magnetoencephalogram recordings. |
| abstract
(cze) |
V clanku je navrzen vypocetne nenarocny algoritmus, ktery kombinuje schopnosti slepe separace dvou odlisnych algorimu: EFICA, ktery vyuziva ne-gaussovskosti rozlozeni jednotlivych separovanych zdroju, a WASOBI, ktery vyuziva rozdilnosti frekvencnich spekter jednotlivych zdroju. Algoritmus je vhodny pro analyzu mnohadimensionalnich dat z elektroencefalogramu a magnetoencefaloogramu s vysokym rozlisenim. |
| action |
| ARLID |
cav_un_auth*0230095 |
| name |
EUSIPCO 2007: The 15th European Signal Processing Conference |
| place |
Poznan |
| dates |
03.09.2007-07.09.2007 |
| country |
PL |
|
| reportyear |
2008 |
| RIV |
FH |
| permalink |
http://hdl.handle.net/11104/0148362 |
| arlyear |
2007 |
| mrcbU56 |
textový soubor 367 kB |
| mrcbU63 |
cav_un_epca*0086003 Proccedings of the 15th European Signal Processing Conference. EUSIPCO 2007 978-83-921340-2-2 1731 1735 Poznan PTETiS 2007 |
|