Akan, AydınÖzkan Seyhan, TülayGÜRKAN, GÜRAY2018-07-172018-07-1720141051-2004https://doi.org/10.1016/j.dsp.2013.11.011https://hdl.handle.net/11413/2135In this paper we present a method for the analysis of multichannel EEG by using Generalized Partial Directed Coherence (gPDC) to extract cortical connectivity changes under anesthesia. 15 channel EEG data were recorded from female subjects undergoing general anesthesia for gynecological surgery. Multivariate Autoregressive (MAR) modeling was applied to multichannel, bipolar EEG segments of awake and anesthetized states. gPDCs were calculated using the derived MAR model and averaged through EEG alpha frequency band (8-14 Hz) and through a number of data segments. The gPDC calculation was performed for three different sets of bipolar EEG channel pairs each of which mainly represent a specific scalp area. To derive consistency levels of gPDC values, surrogate analysis is also performed. Using paired t-test for 12 patients, we extracted significant gPDC changes between. awake and anesthetized stages for each set. Analysis revealed that during transition from awake to anesthetized stage, gPDCs of central. to parietal directions were suppressed whereas gPDCs of parietal to central directions were increased. The results indicate that the proposed gPDC analysis method can be used to track the changes in brain connectivity and hence to estimate the depth of anesthesia. (C) 2013 Elsevier Inc. All rights reserved.en-USMultivariate autoregressive modelingGranger causalityFrontal alpha oscillationAnesthesiaGeneralized partial directed coherenceArticle3302594000143302594000142-s2.0-848923657932-s2.0-84892365793