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- W2009052468 abstract "Electroencephalography (EEG) and magnetoencephalography (MEG) have excellent time resolution. However,the poor spatial resolution and small number of sensors do not permit to reconstruct a general spatial activationpattern. Moreover, the low signal to noise ratio (SNR) makes accurate reconstruction of a time course alsochallenging. We therefore propose to use constrained reconstruction, modeling the relevant part of the brainusing a neural mass model: There is a small number of zones that are considered as entities, neurons within a zoneare assumed to be activated simultaneously. The location and spatial extend of the zones as well as the interzonalconnection pattern can be determined from functional MRI (fMRI), diffusion tensor MRI (DTMRI), andother anatomical and brain mapping observation techniques. The observation model is linear, its deterministicpart is known from EEG/MEG forward modeling, the statistics of the stochastic part can be estimated. Thedynamics of the neural model is described by a moderate number of parameters that can be estimated from therecorded EEG/MEG data. We explicitly model the long-distance communication delays. Our parameters havephysiological meaning and their plausible range is known. Since the problem is highly nonlinear, a quasi-Newtonoptimization method with random sampling and automatic success evaluation is used. The actual connectiontopology can be identified from several possibilities. The method was tested on synthetic data as well as on trueMEG somatosensory-evoked field (SEF) data." @default.
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- W2009052468 date "2007-03-08" @default.
- W2009052468 modified "2023-09-23" @default.
- W2009052468 title "Neural mass model parameter identification for MEG/EEG" @default.
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- W2009052468 doi "https://doi.org/10.1117/12.709146" @default.
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