By Todd C. Handy
Fresh advancements within the instruments and methods of knowledge acquisition and research in cognitive electrophysiology.
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Extra info for Brain Signal Analysis: Advances in Neuroelectric and Neuromagnetic Methods
Koenig et al. 2002; Katayama et al. , Pegna et al. 1997; Overnay et al. 2005; Thierry et al. 2007). 4 shows the outcome of the AAHC clustering and ﬁtting procedure when applied to the VEPs presented throughout this chapter. 4 Topographic pattern analyses applied to VEPs in response to initial and repeated presentations of visual objects. (a) The outcome of the clustering algorithm (AAHC) applied to the groupaveraged VEPs. Shaded regions indicate time periods when different template maps were identiﬁed across experimental conditions.
The Krzanowski–Lai criterion works by ﬁrst computing a quality measure of the AAHC, termed dispersion (W). W tends toward 0 as the quality of the clustering results increases, in much the same manner that the GEV tends toward 1 as the quality of the clustering improves. The shape of the resulting W curve is then analyzed by looking for its L-corner, which is the point of highest deceleration where adding one more segment will not greatly increase the quality of the results. 2). As a consequence its highest value should, in principle, indicate the optimal clustering.
Even if the single dipole may not adequately or accurately represent the actual sources in case of distributed activation patterns, a statistically signiﬁcant shift of the “center of gravity” of all sources due to an experimental manipulation can provide indications of a change of the global activity distribution within the brain. Whether this analysis also allows interpretation about the brain areas that have been differently activated will ultimately depend on how accurately the dipole model represents the underlying generators.
Brain Signal Analysis: Advances in Neuroelectric and Neuromagnetic Methods by Todd C. Handy