Combining complexity measures of EEG data: multiplying measures reveal previously hidden information
Many studies have noted significant differences among human EEG results when participants or patients are exposed to different stimuli, undertaking different tasks, or being affected by conditions such as epilepsy or Alzheimer's disease. Such studies often use only one or two measures of complexity and do not regularly justify their choice of measure beyond the fact that it has been used in previous studies. If more measures were added to such studies, however, more complete information might
... information might be found about these reported differences. Such information might be useful in confirming the existence or extent of such differences, or in understanding their physiological bases. In this study I analysed publically-available EEG data using a range of complexity measures to determine how well the measures correlated with one another. The complexity measures did not all significantly correlate, suggesting that different measures were measuring unique features of the EEG signals and thus revealing information which other measures were unable to detect. Therefore, the results from this analysis suggests that combinations of complexity measures reveal unique information which is in addition to the information captured by other measures of complexity in EEG data. For this reason, researchers using individual complexity measures for EEG data should consider using combinations of measures to more completely account for any differences they observe and to ensure the robustness of any relationships identified.