Identifying EEG Responses Modulated by Working Memory Loads from Weighted Phase Lag Index Based Functional Connectivity Microstates

Decreased connection density and modularity of functional brain networks during n‐back working memory paradigm

“…However, in that study, the authors only used epochs of 2.5 s taken from the beginning of each stimulation block, in addition to estimating the graph theoretical measures first and averaged them over various difficulty settings subsequently. As shown previously, that FC characteristics can vary significantly in different phases of various WM tasks (Ren et al., 2017; Toppi et al., 2018; Zhang et al., 2019); therefore, it is plausible that the functional organization in the brain at the beginning of each stimulation block is different from later periods. Also, in our analytical pipeline, the obtained synchronization matrices were first averaged over the stimulation blocks of corresponding difficulty levels, and network measures were only then calculated from the average connectivity matrices.…”

A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer’s Disease Using EEG Signals

“…Weighted phase lag index (PLI) is a variation of phase lag index that entails weighting the PLI rates by the imaginary portion of the cross-spectrum between the two timeseries [77]; the latter part of the cross-spectrum is related to the phase difference, or delay, between the signals. The two signs are nearly overlapping if the imaginary component is close to 0.…”