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

Autor: Gilney Figueira Zebende, Florêncio Mendes Oliveira Filho, Juan Alberto Leyva Cruz
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Man-Computer Interface
Male
Databases
Factual
Computer science
Physiology
Sensory Physiology
lcsh:Medicine
Electroencephalography
01 natural sciences
010305 fluids & plasmas
Parietal Lobe
Medicine and Health Sciences
Right Hemisphere
lcsh:Science
Clinical Neurophysiology
Cerebral Cortex
Brain Mapping
Multidisciplinary
medicine.diagnostic_test
Parietal lobe
Brain
Signal Processing
Computer-Assisted
White noise
Human brain
Sensory Systems
Electrophysiology
Amplitude
medicine.anatomical_structure
Bioassays and Physiological Analysis
Brain Electrophysiology
Somatosensory System
Engineering and Technology
Female
Anatomy
Algorithms
Research Article
Imaging Techniques
Neurophysiology
Neuroimaging
Research and Analysis Methods
Ocular System
0103 physical sciences
medicine
Humans
Left Hemisphere
010306 general physics
business.industry
Autocorrelation
Electrophysiological Techniques
lcsh:R
Biology and Life Sciences
Pain Sensation
Pattern recognition
Function (mathematics)
Human Factors Engineering
Detrended fluctuation analysis
Eyes
lcsh:Q
Artificial intelligence
Clinical Medicine
business
Head
Cerebral Hemispheres
Neuroscience
Zdroj: PLoS ONE, Vol 12, Iss 9, p e0183121 (2017)
PLoS ONE
ISSN: 1932-6203
Popis: 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.
Databáze: OpenAIRE
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