TY - JOUR
T1 - A general statistical framework for frequency-domain analysis of EEG topographic structure
AU - Kelly, Edward F.
AU - Lenz, James E.
AU - Franaszczuk, Piotr J.
AU - Truong, Young K.
PY - 1997/4
Y1 - 1997/4
N2 - 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.
AB - 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.
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U2 - 10.1006/cbmr.1997.1444
DO - 10.1006/cbmr.1997.1444
M3 - Article
C2 - 9167085
AN - SCOPUS:0031127290
SN - 0010-4809
VL - 30
SP - 129
EP - 164
JO - Computers and Biomedical Research
JF - Computers and Biomedical Research
IS - 2
ER -