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Illustration of spectral EEG pattern similarity analysis. A. Representational similarity is operationalized as pairwise correlations of the frequency patterns at each trial time point, separately for each electrode and subject. B. The resulting time-time similarity matrices contain the individual similarity at all trial time point combinations and can be averaged across trials, electrodes, and/or participants, and compared between conditions or groups, for instance. To assess differences in similarity patterns across conditions or groups, non-parametric cluster-based permutation statistics can be applied. Figure adapted from Sommer et al., 2019.

Illustration of spectral EEG pattern similarity analysis. A. Representational similarity is operationalized as pairwise correlations of the frequency patterns at each trial time point, separately for each electrode and subject. B. The resulting time-time similarity matrices contain the individual similarity at all trial time point combinations and can be averaged across trials, electrodes, and/or participants, and compared between conditions or groups, for instance. To assess differences in similarity patterns across conditions or groups, non-parametric cluster-based permutation statistics can be applied. Figure adapted from Sommer et al., 2019.

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The human brain encodes information in neural activation patterns. While standard approaches to analyzing neural data focus on brain (de-)activation (e.g., regarding the location, timing, or magnitude of neural responses), multivariate neural pattern similarity analyses target the informational content represented by neural activity. In adults, a n...

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... tutorial provides comprehensive step-by-step instructions that detail all necessary computations to conduct multivariate neural pattern similarity analyses on time-frequency-resolved EEG data (as recently applied in Sommer et al., 2019, see Figure 2 below for a schematic illustration). Furthermore, we demonstrate how cluster-based permutation statistics (Maris and Oostenveld, 2007) can be used to ascertain differences in neural patterns across representational levels and age groups. ...
Context 2
... identified cluster dimension can be used to extract similarity values from those channel × time × time coordinates in which differences were shown to be reliable (see Figure 6). Figure 2) plus outlines of identified clusters (see Figure 5) showing at what time-time coordinates within-item (left) and within-category (right) similarities may show reliable differences, averaged across electrodes, for children (top) and adults (bottom). These images can be created with step5_plot_clusters. ...

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Article
Long-standing theories of cognitive aging suggest that memory decline is associated with age-related differences in the way information is neurally represented. Multivariate pattern similarity analyses enabled researchers to take a representational perspective on brain and cognition, and allowed them to study the properties of neural representations that support successful episodic memory. Two representational properties have been identified as crucial for memory performance, namely the distinctiveness and the stability of neural representations. Here, we review studies that used multivariate analysis tools for different neuroimaging techniques to clarify how these representational properties relate to memory performance across adulthood. While most evidence on age differences in neural representations involved stimulus category information , recent studies demonstrated that particularly item-level stability and specificity of activity patterns are linked to memory success and decline during aging. Overall, multivariate methods offer a versatile tool for our understanding of age differences in the neural representations underlying memory.