Volume 14, Issue 4 (July & August 2023)
BCN 2023, 14(4): 519-528 |
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Mehdizadehfar V, Ghassemi F, Fallah A. Brain Connectivity Estimation Pitfall in Multiple Trials of Electroencephalography Data. BCN 2023; 14 (4) :519-528
URL: http://bcn.iums.ac.ir/article-1-2215-en.html
URL: http://bcn.iums.ac.ir/article-1-2215-en.html
1- Department of Bioelectric, School of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.
Abstract:
Introduction: The electroencephalography signal is well suited to calculate brain connectivity due to its high temporal resolution. When the purpose is to compute connectivity from multi-trial electroencephalography (EEG) data, confusion arises about how these trials involved in calculating the connectivity. The purpose of this paper is to study this confusing issue using simulated and experimental data.
Methods: To this end, Granger causality-based connectivity measures were considered. Using simulations, two signals were generated with known AR (auto-regressive) coefficients and then simple multivariate autoregressive (MVAR) models based on different numbers of trials were extracted. For accurate estimation of the MVAR model, the data samples should be sufficient. Two Granger causality-based connectivity, granger causality (GC) and Partial directed coherence (PDC) were estimated.
Results: Estimating connectivity corresponding to small trial numbers (5 and 10 trials) resulted in an average value of connectivity that is significantly higher and also more variable over different estimates. By increasing the number of trials, the MVAR model has fitted more appropriately to the data and the connectivity values were converged. This procedure was implemented on real EEG data. The obtained results agreed well with the findings of simulated data.
Conclusion: The results showed that the brain connectivity should calculate for each trial, and then average the connectivity values on all trials. Also, the larger the trial numbers, the MVAR model has fitted more appropriately to the data, and connectivity estimations are more reliable.
Methods: To this end, Granger causality-based connectivity measures were considered. Using simulations, two signals were generated with known AR (auto-regressive) coefficients and then simple multivariate autoregressive (MVAR) models based on different numbers of trials were extracted. For accurate estimation of the MVAR model, the data samples should be sufficient. Two Granger causality-based connectivity, granger causality (GC) and Partial directed coherence (PDC) were estimated.
Results: Estimating connectivity corresponding to small trial numbers (5 and 10 trials) resulted in an average value of connectivity that is significantly higher and also more variable over different estimates. By increasing the number of trials, the MVAR model has fitted more appropriately to the data and the connectivity values were converged. This procedure was implemented on real EEG data. The obtained results agreed well with the findings of simulated data.
Conclusion: The results showed that the brain connectivity should calculate for each trial, and then average the connectivity values on all trials. Also, the larger the trial numbers, the MVAR model has fitted more appropriately to the data, and connectivity estimations are more reliable.
Keywords: Connectivity analysis, Multiple trials, Auto-regressive model, Granger causality (GC), Electroencephalography (EEG)
Type of Study: Original |
Subject:
Computational Neuroscience
Received: 2021/04/6 | Accepted: 2021/08/21 | Published: 2023/07/1
Received: 2021/04/6 | Accepted: 2021/08/21 | Published: 2023/07/1
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