| Zugriffsnummer | 22903 |
| Dokumenttyp | Dissertation |
| Peer Review | unbekannt |
| Sprache | Englisch |
| Titel | Evaluation and comparison of the independent components of simultaneously measured MEG and EEG data |
| Autor(in); Institution |
Zavala-Fernandez, Heriberto; 8.2, Biosignale, PTB-Berlin
|
| Quelle/Jahr | (2009), 117 S. |
| Dissertationsvermerk | Dissertation, Technische Universität Berlin, 2009 |
| ISBN | 978-3-7983-2122-9 |
| Verlag | Berlin: Universitätsverlag |
| Zusammenfassung | Magnetoencephalography (MEG) and Electroencephalography (EEG) are widely used methods for the study of neural activity. This activity can be represented as a linear superposition of different signals generated by the processes active in the brain as well as, external signals generated by sources not belonging to the brain. Given this framework, Independent Component Analysis (ICA) is a well positioned technique for the analysis and comprehension of brain behavior. ICA has been already successfully applied to many brain activity studies employing either MEG or EEG signals in unimodal approaches. However, the complementarity of MEG and EEG recordings, due to the fact that magnetic fields and electric potentials arise from the same source, is well-known.Taking this into account, it is started from the premise that simultaneous recordings of MEG and EEG data investigated in the context of bimodal ICA, have the potential to improve the source isolation and localization by exploiting their complementarity. Bimodal ICA of simultaneous recordings also eliminates environmental confounds inherent to separate analysis of MEG and EEG. In this PhD thesis, it is presented a technique to exploit the signal complementarity in the bimodal subspace formed by MEG and EEG recordings by applying different ICA methods. Due to the scaling ambiguity inherent to the results of bimodal ICA, it is not possible to characterize whether a bimodal ICA component corresponds to one (unimodal source) or both modalities (bimodal source). For solving this problem, a procedure is proposed termed as "Single Modality Demixing" (SMD). The bimodal ICA components are validated by computing the corresponding Equivalent Current Dipoles (ECD) of selected sources, e.g. Alpha and MU oscillations, Auditory Evoked Responses (AER). Additionally, a concatenation of epochs containing the AER are subjected to the unimodal and bimodal ICA. In general, bimodal ICA produces similar patterns compared to unimodal ICA. Moreover, ECDs correspnding to the sources computed by bimodal ICA were more accurate than those computed by unimodal ICA. |
