Brain Signal Analysis: Advances in Neuroelectric and by Todd C. Handy

By Todd C. Handy

Contemporary advancements within the instruments and methods of knowledge acquisition and research in cognitive electrophysiology.

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However, it has the disadvantage of varying from one run to the other because of the iterative randomization nature of the algorithm (see Murray et al. 2008a). 2). As such, its results will not vary from one run to another with the same data set. Hierarchical clustering takes the instantaneous GFP of the data being clustered into consideration when calculating which clusters to retain and therefore preferentially considers as robust clusters time periods with higher signal quality. Because ERPs typically consist of series of GFP peaks and troughs (similar to those observed in voltage waveforms), hierarchical clustering seems to be well suited for the analysis of ERPs, whereas k-means clustering has advantages in data with less distinct peaks, such as spontaneous EEG.

30 Chapter 2 stronger in condition 2. , the value at each electrode was multiplied by −1). , topography) of the same values. Note that condition 3 is included to illustrate an extreme case that is unlikely under typical experimental conditions. Condition 4, by contrast, represents a more typical observation in that it varies in both strength and topography from condition 1. It is in fact a re-distribution in space of the values used for condition 2. 2b displays the squared value of these potentials at each electrode, the sum of these values across electrodes, and the resultant GFP.

Instead TANOVA entails a nonparametric randomization test (Manly 1991). , permutations of the data), (2) recalculating the group-average ERPs, and (3) recalculating the resulting DISS value for these “new” 32 Chapter 2 group-average ERPs. The number of permutations that can be made with a groupaverage ERP based on n participants is 2n, although Manly (1991) suggests that 1,000 to 5,000 permutations is sufficient. The DISS value from the actual group-average ERPs is then compared with the values from the empirical distribution to determine the likelihood that the empirical distribution has a value higher than the DISS from the actual group-average ERPs.

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