Combining complexity measures of EEG data: multiplying measures reveal previously hidden information.

ABSTRACT: Many studies have noted significant differences among human electroencephalograph (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 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 we analysed publically-available EEG data using a range of complexity measures to

SUBMITTER: Burns T 

PROVIDER: S-EPMC4648221 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature