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date: 18 May 2021

Abstract and Keywords

This chapter provides a primer on source estimation, connectivity, and multivariate pattern analysis for electro- and magnetoencephalography (EEG and MEG) data. The excellent temporal resolution of EEG and MEG allows investigating brain responses in five dimensions (space, time, and frequency), which provides unique opportunities for connectivity and multivariate pattern analysis methods. However, the application of these methods leads to several methodological challenges. This chapter describes some of the opportunities and challenges for source estimation, connectivity, and multivariate pattern analysis of EEG/MEG data. It illustrates their usefulness to address important questions from the neuroscience of language on the basis of recent studies in two research areas: the role of phonology in visual word recognition, and the brain networks underlying semantic word processing. It also briefly describes some recent developments in EEG/MEG research on speech processing and in multimodal neuroimaging of language functions.

Keywords: EEG, MEG, source estimation, connectivity, multivariate pattern analysis, MVPA, representational similarity analysis, RSA, phonology, semantics

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