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- W2072669013 endingPage "164" @default.
- W2072669013 startingPage "129" @default.
- W2072669013 abstract "A wide variety of rhythmic electrophysiological phenomena—including driven, induced, and endogenous activities of cortical neuronal masses—lend themselves naturally to analysis using frequency-domain techniques applied to multichannel recordings that discretely sample the overall spatial pattern of the rhythmic activity. For such cases, a large but so far poorly utilized body of statistical theory supports a third major approach to topographic analysis, complementing the more familiar mapping and source-recovery techniques. These methods, many of which have only recently become computationally feasible, collectively provide general solutions to the problem of detecting and characterizing systematic differences that arise—not only in the spatial distribution of the activity, but also in its frequency-dependent between-channel covariance structure—as a function of multiple experimental conditions presented in conformity with any of the conventional experimental designs. This application-oriented tutorial review provides a comprehensive outline of these resources, including: (1) real multivariate analysis of single-channel spectral measures (and measures of between-channel relationships such as coherence and phase), (2) complex multivariate analysis based on multichannel Fourier transforms, and (3) complex multivariate analysis based on multichannel parametric models. Special emphasis is placed on the potential of the multichannel autoregressive model to support EEG (and MEG) studies of perceptual and cognitive processes." @default.
- W2072669013 created "2016-06-24" @default.
- W2072669013 creator A5061476112 @default.
- W2072669013 creator A5074996041 @default.
- W2072669013 creator A5076367797 @default.
- W2072669013 creator A5089264754 @default.
- W2072669013 date "1997-04-01" @default.
- W2072669013 modified "2023-10-18" @default.
- W2072669013 title "A General Statistical Framework for Frequency-Domain Analysis of EEG Topographic Structure" @default.
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