Auto-correlation in the motor/imaginary human EEG signals: A vision about the FDFA fluctuations.
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 da...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5598924?pdf=render |
| _version_ | 1811319667090784256 |
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| author | Gilney Figueira Zebende Florêncio Mendes Oliveira Filho Juan Alberto Leyva Cruz |
| author_facet | Gilney Figueira Zebende Florêncio Mendes Oliveira Filho Juan Alberto Leyva Cruz |
| author_sort | Gilney Figueira Zebende |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T12:46:43Z |
| format | Article |
| id | doaj.art-8ca6cbb580ea4471a4a6cbf6b56699bd |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-13T12:46:43Z |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-8ca6cbb580ea4471a4a6cbf6b56699bd2022-12-22T02:46:21ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01129e018312110.1371/journal.pone.0183121Auto-correlation in the motor/imaginary human EEG signals: A vision about the FDFA fluctuations.Gilney Figueira ZebendeFlorêncio Mendes Oliveira FilhoJuan Alberto Leyva CruzIn 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.http://europepmc.org/articles/PMC5598924?pdf=render |
| spellingShingle | Gilney Figueira Zebende Florêncio Mendes Oliveira Filho Juan Alberto Leyva Cruz Auto-correlation in the motor/imaginary human EEG signals: A vision about the FDFA fluctuations. PLoS ONE |
| title | Auto-correlation in the motor/imaginary human EEG signals: A vision about the FDFA fluctuations. |
| title_full | Auto-correlation in the motor/imaginary human EEG signals: A vision about the FDFA fluctuations. |
| title_fullStr | Auto-correlation in the motor/imaginary human EEG signals: A vision about the FDFA fluctuations. |
| title_full_unstemmed | Auto-correlation in the motor/imaginary human EEG signals: A vision about the FDFA fluctuations. |
| title_short | Auto-correlation in the motor/imaginary human EEG signals: A vision about the FDFA fluctuations. |
| title_sort | auto correlation in the motor imaginary human eeg signals a vision about the fdfa fluctuations |
| url | http://europepmc.org/articles/PMC5598924?pdf=render |
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