Spatiotemporal Searchlight Representational Similarity Analysis in EMEG Source Space

Time resolved imaging techniques, such as MEG and EEG, are unique in their ability to reveal the rich dynamic spatiotemporal patterning of neural activities. Here we propose a technique based on spatiotemporal searchlight Representational Similarity Analysis (RSA) of combined MEG and EEG (EMEG) data...

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Bibliographic Details
Published in2012 International Workshop on Pattern Recognition in NeuroImaging pp. 97 - 100
Main Authors Li Su, Fonteneau, E., Marslen-Wilson, W., Kriegeskorte, N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2012
Subjects
Brain modeling
cohort model
Computational modeling
Correlation
Data models
EEG
Electroencephalography
Estimation
MEG
MNE
MVPA
RSA
Spatiotemporal phenomena
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ISBN1467321826
9781467321822
DOI10.1109/PRNI.2012.26

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Summary:Time resolved imaging techniques, such as MEG and EEG, are unique in their ability to reveal the rich dynamic spatiotemporal patterning of neural activities. Here we propose a technique based on spatiotemporal searchlight Representational Similarity Analysis (RSA) of combined MEG and EEG (EMEG) data to directly analyze the multivariate pattern of information flow across the brain. This novel technique can recognize fine-grained dynamic neural computations both in space and in time. A prime example of such neural computations is our ability to understand spoken words in real time. A computational approach to these processes is suggested by the Cohort Model of spoken-word recognition. Here we show how spatiotemporal searchlight RSA applied to source estimations of EMEG data can provide insights into the neural correlates of the cohort model within bilateral front temporal brain regions.
ISBN:1467321826
9781467321822
DOI:10.1109/PRNI.2012.26