Function of EEG temporal complexity analysis in neural activities measurement

Abstract

We investigate the correlation between temporal complexity of EEG signal and the underlining neural activities. Fractal geometry has been proved useful in quantifying complexities of dynamical signals. Temporal fractal dimension of EEG signals provides a new neurophysiological measure. In order to better understand what the complexity measure reveals about the underling brain process, a further exploration on the neuronal generators of fractal geometry characteristics of EEG is conducted in this study. Our investigation suggests that the temporal fractal measure of EEG signals can be related to the activity diversity of neuronal population activities. The complexity measure also gives an indication on the change in synchronization state under certain mental conditions. These assumptions are supported by experimental evidence from the visual cortex and sensorimotor cortex. This work helps give an interpretation of the obtained results of the temporal complexity analysis on EEG signals and may be useful in further investigating the covert steps of brain information processing. © 2009 Springer Berlin Heidelberg.