ICA on sensor or source data: A comparison study in deriving resting state networks from EEG | IEEE Conference Publication | IEEE Xplore

ICA on sensor or source data: A comparison study in deriving resting state networks from EEG


Abstract:

Resting state networks (RSNs) are human brain networks formed by spontaneous activity fluctuations in distributed brain regions when people are in task-free and awake sta...Show More

Abstract:

Resting state networks (RSNs) are human brain networks formed by spontaneous activity fluctuations in distributed brain regions when people are in task-free and awake state. RSNs have been so far extensively studied using functional magnetic resonance imaging (fMRI). Recently, electroencephalography (EEG) and magnetoencephalography (MEG) have also been used to derive RSNs, in which independent component analysis (ICA) is the key step. In these studies, ICA has been either directly applied to recorded data at sensors (sensor-space ICA) or estimated source data from sensors using inverse source imaging techniques (source-space ICA). Both sensor-space and source-space ICAs have demonstrated the capability in finding RSNs from EEG/MEG data and their results showed strong correlations to fMRI RSNs. However, their performance was hardly compared even differences have been observed in their results. In the present study, we compared the source-space and sensor-space ICAs in reconstructing spatial, temporal and spectral features of RSNs in both simulated and real EEG data. Results from simulated data indicated that the source-space ICA has better performance in reconstructing spatial, temporal, and spectral feature of RSNs. Results from resting-sate EEG data in seven healthy participants also showed the difference between two procedures and, through the comparison with RSN templates constructed from fMRI data, the source-space ICA indicated relatively better performance than the sensor-space ICA.
Date of Conference: 11-15 July 2017
Date Added to IEEE Xplore: 14 September 2017
ISBN Information:

ISSN Information:

PubMed ID: 29060678
Conference Location: Jeju, Korea (South)

References

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