Detection of visual information processing regions from high-density EEG data

Autoři: Ing. Anna Pidnebesna, Ph.D.
Ing. Stanislav Jiříček
Mgr. Pavel Šanda, Ph.D.
Ing. Mgr. Jaroslav Hlinka, Ph.D.
Rok vydání: 2021
Typ publikace: Abstrakt
Periodikum: Journal of Computational Neuroscience
Projekty: Dynamika zpracování prostorové scény v dorzální a ventrální zrakové dráze lidského mozku
Bibliografická citace: PIDNEBESNA, Anna, JIŘÍČEK, Stanislav, KOUDELKA, V., VLČEK, Kamil, ŠANDA, Pavel, HAMMER, J., HLINKA, Jaroslav. Detection of visual information processing regions from high-density EEG data. Journal of Computational Neuroscience. 2021, 49(Suppl. 1), S89-S90. ISSN 0929-5313. E-ISSN 1573-6873.
ID: 554060

Abstrakt

Visual information processing plays an important role in human perception and cognition. Measuring the information flow is an even more challenging task than purely detecting local activations. The selection of parsimoneous set of relevant regions of interest (ROIs) is key for successful analysis. A common choice is using blind source separation (ICA, PCA, NNMF). However, due to nonstationarity of the stimulus driven data and multiple local maxima of the temporal components, interpretable description of spreading of the initial stimulus is complicated. We thus propose a method that enforces better temporal localization of the activity within the studied ROIs, and demonstrate an application to source-reconstructed high-density EEG data.