Auto-correlation in the motor/imaginary human EEG signals: A vision about the F(DFA) fluctuations

In this paper we analyzed, by the F(DFA) root mean square fluctuation (rms) function, the motor/imaginary human activity produced by a 64-channel electroencephalography (EEG). We utilized the Physionet on-line databank, a publicly available database of human EEG signals, as a standardized reference database for this study. Herein, we report the use of detrended fluctuation analysis (DFA) method for EEG analysis. We show that the complex time series of the EEG exhibits characteristic fluctuations depending on the analyzed channel in the scalp-recorded EEG. In order to demonstrate the effectiveness of the proposed technique, we analyzed four distinct channels represented here by F(3)32, F(6)37 (frontal region of the head) and P(3)49, P(6)54 (parietal region of the head). We verified that the amplitude of the F(DFA) rms function is greater for the frontal channels than for the parietal. To tabulate this information in a better way, we define and calculate the difference between F(DFA) (in log scale) for the channels, thus defining a new path for analysis of EEG signals. Finally, related to the studied EEG signals, we obtain the auto-correlation exponent, α(DFA) by DFA method, that reveals self-affinity at specific time scale. Our results shows that this strategy can be applied to study the human brain activity in EEG processing.