Bibliography

(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.

(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.