Weighted Phase Lag Index (WPLI) as a Method for Identifying Task- Related Functional Networks in Electroencephalography (EEG) Recordings during a Shooting Task.

摘要

Weighted Phase Lag Index (WPLI) is a methodology used to identify nonzero phase lag statistical interdependencies between electroencephalography (EEG) time series from pairs of electrodes. Identifying nonzero phase lags may be useful to identify neural interaction among regions based on the known delay for brain region-to-region communication. This project applies WPLI analysis to previously collected EEG data from a Soldier performing a shooting task. WPLI was examined on EEG data epoched around trigger pull events, which includes both brain activity and movement artifacts from weapon recoil. This functional connectivity measure was compared to a traditional time-frequency analysis at individual electrode sites. In the individual studied, WPLI identified a left lateralization in the network communication as well as neural activity from the occipital electrodes. Both of these findings were not evident in the channel- based analysis. This project suggests that WPLI may be able to detect task- related functional networks, despite artifactual contamination that is typical in real-world environments.