Kereső
Bejelentkezés
Kapcsolat
Nonparametric statistical testing of functional connectivity in EEG data |
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: |