Review Paper

Cognitive Neurodynamics

, Volume 8, Issue 1, pp 1-15

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

Time domain measures of inter-channel EEG correlations: a comparison of linear, nonparametric and nonlinear measures

  • J. D. BonitaAffiliated withDepartment of Physics, Mindanao State University-Iligan Institute of Technology
  • , L. C. C. AmbolodeIIAffiliated withDepartment of Physics, Mindanao State University-Iligan Institute of Technology
  • , B. M. RosenbergAffiliated withThomas Jefferson University College of Medicine
  • , C. J. CellucciAffiliated withAquinas, LLC
  • , T. A. A. WatanabeAffiliated withLannister-Finn
  • , P. E. RappAffiliated withDepartment of Military and Emergency Medicine, Uniformed Services University of the Health Sciences Email author 
  • , A. M. AlbanoAffiliated withPhysics Department, Bryn Mawr College


Correlations between ten-channel EEGs obtained from thirteen healthy adult participants were investigated. Signals were obtained in two behavioral states: eyes open no task and eyes closed no task. Four time domain measures were compared: Pearson product moment correlation, Spearman rank order correlation, Kendall rank order correlation and mutual information. The psychophysiological utility of each measure was assessed by determining its ability to discriminate between conditions. The sensitivity to epoch length was assessed by repeating calculations with 1, 2, 3, …, 8 s epochs. The robustness to noise was assessed by performing calculations with noise corrupted versions of the original signals (SNRs of 0, 5 and 10 dB). Three results were obtained in these calculations. First, mutual information effectively discriminated between states with less data. Pearson, Spearman and Kendall failed to discriminate between states with a 1 s epoch, while a statistically significant separation was obtained with mutual information. Second, at all epoch durations tested, the measure of between-state discrimination was greater for mutual information. Third, discrimination based on mutual information was more robust to noise. The limitations of this study are discussed. Further comparisons should be made with frequency domain measures, with measures constructed with embedded data and with the maximal information coefficient.


EEG Quantitative EEG Pearson product moment correlation Spearman rank order correlation Kendall rank order correlation Mutual information