Institutional Repository
Technical University of Crete
Technical University of Crete
EN
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EL
| URI: | http://purl.tuc.gr/dl/dias/DAB48745-66B3-4AC4-8370-176177EC774F | ||
| Year | 2019 | ||
| Type of Item | Diploma Work | ||
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| Bibliographic Citation | Konstantinos Politof, "Functional connectivity in the wrist somatosensory network: an EEG/MEG study", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2019 https://doi.org/10.26233/heallink.tuc.82235 | ||
| Appears in Collections |
Motivation: We attempt to cover the lack of an integrated way to describe the early interactions within the primary somatosensory network. A combination of EEG and MEG (EMEG) has been shown to outperform single EEG or MEG in source analysis. EMEG may be a promising integrated way for the goal of the current study.Objective: The current study investigates the time-variant connectivity network of the primary somatosensory cortex by means of a functional source separation approach and source analysis of different measurement modalities.Methods: The brain signals are recorded by the non-invasive modalities of electro- and magneto- encephalography (EEG and MEG) on a healthy subject, who participated in an experiment for measuring somatosensory evoked responses by median nerve stimulations on the right wrist. After the prepossessing, time-locked analysis (TLA) is applied for the reduction of the non-cerebral activity in both, EEG and MEG. After the estimation of the somatosensory evoked potentials (SEP) and fields (SEF), the goal was the extraction of the time-functional (or functional) components. The separation of the somatosensory functional components is accomplished by a semi-blind algorithm, the functional source separation (FSS), which uses a priori information of each functional component to extract the functional sources (FSs) for each modality. The back-projected SEP and SEF responses are calculated for each functional source. The EMEG measurement modality is estimated by these EEG and MEG back-projected data of the same components. Then, for every back-projected data of each modality (EEG, MEG or EMEG) and for each of their time points we find a solution to source localization by using the sLORETA algorithm and we obtain the source waveforms. The used individual and realistic head model includes six tissue compartments (scalp, skull compacta, skull spongiosa, cerebrospinal fluid, gray and white matter), brain anisotropy and calibrated skull conductivities. The source waveforms all of the modalities were set the base for the estimation of the effective and time-varying primary somatosensory connectivity network using time-varying Generalized Orthogonalized Partial Directed Coherence (tv-GOPDC).
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