Topographical assessment of neurocortical connectivity by using directed transfer function and partial directed coherence during meditation

Cognitive Processing
Laxmi Shaw, Aurobinda Routray

Abstract

Due to the presence of nonlinearity and volume conduction in electroencephalography (EEG), sometimes it's challenging to find out the actual brain network from neurodynamical alteration. In this paper, two well-known time-frequency brain connectivity measures, namely partial directed coherence (PDC) and directed transfer function (DTF), have been applied to evaluate the performance analysis of EEG signals obtained during meditation. These measures are implemented to the multichannel meditation EEG data to get the directed neural information flow. Mostly the assessment of PDC and DTF is entirely subjective and there are probabilities to have erroneous connectivity estimation. To avoid the subjective evaluation, the performance results are compared in terms of absolute energy, signal-to-noise ratio (SNR) and relative SNR (R-SNR) scale. In most of the cases, the PDC result is found to be more efficient than DTF. The limitation of DTF and PDC in terms of the time-varying multivariate autoregressive (MVAR) model is highlighted. The time-varying MVAR model can track the neurodynamical changes better than any other method. In the present study, we would like to show that the PDC-based connectivity gives a better understanding of the n...Continue Reading

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Citations

Oct 3, 2018·Journal of Clinical Medicine·Gloria MartínezMohamed Elgendi
Aug 28, 2020·Frontiers in Systems Neuroscience·Camila Sardeto DeolindoElisa Harumi Kozasa

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