Functional Imaging of Brain Activity and Connectivity with MEG
作者： Felix Darvas , Richard M Leahy
DOI: 10.1007/978-3-540-71512-2_7
关键词: Pattern recognition 、 Functional imaging 、 Brain activity and meditation 、 Inverse 、 Signal processing 、 Artificial intelligence 、 Permutation 、 Resting state fMRI 、 Computer science 、 Inverse problem 、 Electroencephalography
摘要: We present a survey of imaging and signal processing methods that use data from magnetoencephalographic (MEG) or electroencephalographic (EEG) measurements to produce spatiotemporal maps neuronal activity as well measures functional connectivity between active brain regions. During the course chapter, we give short introduction basic bioelectromagnetic inverse problem number have been developed solve this problem. discuss address statistical relevance solutions, which is especially important if are used compute inverse. For such permutation can be identify regions interest, subsequently for analysis connectivity. The third section chapter reviews collection commonly in EEG MEG analysis, emphasizing their restrictions advantages applicability time series extracted solutions.
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