Auto-correlation in the motor/imaginary human EEG signals: A vision about the FDFA fluctuations

Abstract

In this paper we analyzed, by the FDFA 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 F332, F637 (frontal region of the head) and P349, P654 (parietal region of the head). We verified that the amplitude of the FDFA 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 FDFA (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, αDFA 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.

Rhythmic activities of the brain: Quantifying the high complexity of beta and gamma oscillations during visuomotor tasks

“…21,34 We have considered the EEG Motor Movement/Imagery Dataset recorded using BCI2000 21,34 instrumentation system available through Physionet. 21,34,[40][41][42] The experimental setup of the BCI2000 system 21,34 includes an arrangement of 64 electrodes used to record the electrical activity of the brain through the EEG signals while the subjects perform different tasks of visuomotor or imagery characteristics 21,34,[41][42][43][44] (see the Appendix for further details). The electrical signals from the brain are picked up by small electrodes that are placed on the subject's head.…”

“…1(a) and consists of an arrangement of different used electrodes. 21,34,[41][42][43][44] Subjects were asked to perform different motor/imagery tasks, while EEG signals were recorded from 64 electrodes along the surface of the scalp. Eye blink artifacts were generated by fast movements of the eyelid along the cornea, such as during an eye blink.…”

“…However, muscular artifacts were carefully checked at the beginning of each recording and verified throughout the recording. 21,34,[41][42][43][44] A number of oscillatory frequency bands have been linked to different, specific aspects of perceptual processing that are of functional importance to understand how information is processed in the human brain. 21,34 Figure 1(b) shows a typical raw signal for one of the electrodes presented in Fig.…”

“…49 Our proposal here is to quantify a variety of oscillatory activity patterns of the EEG signals while performing different tasks of motor or imagery characteristics that were recorded using the BCI2000 system. 21,34,[41][42][43][44] Many studies have shown using EEG that humans can use motor imagery to modulate rhythmic activity of the bands and to thereby control a BCI system (see Ref. 21 and references therein).…”

“…As learning proceeded with further repetitions of the experiment, imagery usually became less important and performance became more automatic. 34,[40][41][42][43][44] Subjects were asked to perform different motor/imagery tasks while EEG signals were recorded from 64 electrodes along the surface of the scalp. Each subject performed 14 experimental runs:…”