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

0554060 - ÚI 2022 RIV NL eng A - Abstract
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. Roč. 49, Suppl. 1 (2021), S89-S90. ISSN 0929-5313. E-ISSN 1573-6873
R&D Projects: GA ČR(CZ) GA19-11753S
Institutional support: RVO:67985807 ; RVO:67985823
Keywords : high-density EEG * information flow * Granger causality * visual processing
OECD category: Neurosciences (including psychophysiology; Neurosciences (including psychophysiology (FGU-C)

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.
Permanent Link: http://hdl.handle.net/11104/0328684