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.
Highlights
The electroencephalogram (EEG) is generally an noninvasive method to record electrical activity of the brain
We provide that the current manuscript advances on previous work, because the EEG signal is mostly analyzed in the frequency domain and here, with detrended fluctuation analysis (DFA) method, we are analyzing the EEG signal in the time domain, which allows us to see directly the time scale
In this paper we propose a new methodology to analyze EEG signals, which are generally treated in the frequency spectrum, by Fourier for example
Summary
The electroencephalogram (EEG) is generally an noninvasive method to record electrical activity of the brain. EEG machine is composed of electrodes, which are placed on the scalp to detect the brain waves [1]. Most EEG machines amplify the signals and records on computer by European Data Format (EDF) file. The EEG measurement is the voltage fluctuations, and with this measure it is possible to diagnose tumors, stroke, epilepsy, and other brain disorders which leads to some abnormalities in EEG readings. Despite the spatial resolution limitations, EEG remains a valuable tool for research and diagnosis, especially when a time resolution interval of milliseconds is required (which is not possible with computed tomography or magnetic resonance imaging) [2, 3]. See [4] for history of EEG.
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