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Nonparametric statistical testing of functional connectivity in EEG data

  • Metaadatok
Tartalom: http://hdl.handle.net/10890/55177
Archívum: Műegyetem Digitális Archívum
Gyűjtemény: 1. Tudományos közlemények, publikációk
Konferenciák gyűjteményei
BME MIT PhD Minisymposium
BME MIT PhD Minisymposium, 2024, 31th
Cím:
Nonparametric statistical testing of functional connectivity in EEG data
Létrehozó:
Vetró, Mihály
Hullám, Gábor
Dátum:
2024-04-30T14:07:39Z
2024-04-30T14:07:39Z
2024
Tartalmi leírás:
The use of nonparametric permutation-based statistical tests for the analysis of functional neuroimaging data (mainly EEG, MEG, and fMRI) is a common approach for comparing observations under different conditions, or in different subject groups. There are variations of these methods to analyze brain signals obtained along the spatial and temporal dimensions, and also across frequency, which accounts for oscillatory activity. However, there is no well-known method for nonparametric testing of the measured functional connectivity between different areas of the brain. In this paper, we introduce a modified version of an existing nonparametric testing framework, which we adapted for use with functional connectivity data. By using this method, we will show that there are brain areas between which patients with Alzheimer’s disease have reduced functional connectivity in an eyes-closed resting state, based on EEG data.
Nyelv:
angol
Típus:
könyvfejezet
Formátum:
application/pdf
Azonosító: