Characterization of EEG Signals Using Wavelet Packet and Fuzzy Entropy in Motor Imagination Tasks
Context: Clinical rhythm analysis on advanced signal processing methods is very important in medical areas such as brain disorder diagnostic, epilepsy, sleep analysis, anesthesia analysis, and more recently in brain-computer interfaces (BCI). Method: Wavelet transform package is used on this work...
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| Format: | Article |
| Language: | Spanish |
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Universidad Distrital Francisco José de Caldas
2017-05-01
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| Series: | Ingeniería |
| Subjects: | |
| Online Access: | http://revistas.udistrital.edu.co/ojs/index.php/reving/article/view/10968 |
| _version_ | 1818791551793364992 |
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| author | Boris Alexander Medina Ramón Alvarez López |
| author_facet | Boris Alexander Medina Ramón Alvarez López |
| author_sort | Boris Alexander Medina |
| collection | DOAJ |
| description | Context: Clinical rhythm analysis on advanced signal processing methods is very important in medical areas such as brain disorder diagnostic, epilepsy, sleep analysis, anesthesia analysis, and more recently in brain-computer interfaces (BCI).
Method: Wavelet transform package is used on this work to extract brain rhythms of electroencephalographic signals (EEG) related to motor imagination tasks. We used the Competition BCI 2008 database for this characterization. Using statistical functions we obtained features that characterizes brain rhythms, which are discriminated using different classifiers; they were evaluated using a 10-fold cross validation criteria.
Results: The classification accuracy achieved 81.11% on average, with a degree of agreement of 61%, indicating a "suitable" concordance, as it has been reported in the literature. An analysis of relevance showed the concentration of characteristics provided in the nodes as a result of Wavelet decomposition, as well as the characteristics that more information content contribute to improve the separability decision region for the classification task.
Conclusions: The proposed method can be used as a reference to support future studies focusing on characterizing EEG signals oriented to the imagination of left and right hand movement, considering that our results proved to compared favourably to those reported in the literature.
Language: Spanish. |
| first_indexed | 2024-12-18T15:13:09Z |
| format | Article |
| id | doaj.art-6d3765207958442ebd2e6761e04bfa40 |
| institution | Directory Open Access Journal |
| issn | 0121-750X 2344-8393 |
| language | Spanish |
| last_indexed | 2024-12-18T15:13:09Z |
| publishDate | 2017-05-01 |
| publisher | Universidad Distrital Francisco José de Caldas |
| record_format | Article |
| series | Ingeniería |
| spelling | doaj.art-6d3765207958442ebd2e6761e04bfa402022-12-21T21:03:35ZspaUniversidad Distrital Francisco José de CaldasIngeniería0121-750X2344-83932017-05-0122222623810.14483/udistrital.jour.reving.2017.2.a048736Characterization of EEG Signals Using Wavelet Packet and Fuzzy Entropy in Motor Imagination TasksBoris Alexander Medina0Ramón Alvarez López1Universidad de SucreUniversidad de SucreContext: Clinical rhythm analysis on advanced signal processing methods is very important in medical areas such as brain disorder diagnostic, epilepsy, sleep analysis, anesthesia analysis, and more recently in brain-computer interfaces (BCI). Method: Wavelet transform package is used on this work to extract brain rhythms of electroencephalographic signals (EEG) related to motor imagination tasks. We used the Competition BCI 2008 database for this characterization. Using statistical functions we obtained features that characterizes brain rhythms, which are discriminated using different classifiers; they were evaluated using a 10-fold cross validation criteria. Results: The classification accuracy achieved 81.11% on average, with a degree of agreement of 61%, indicating a "suitable" concordance, as it has been reported in the literature. An analysis of relevance showed the concentration of characteristics provided in the nodes as a result of Wavelet decomposition, as well as the characteristics that more information content contribute to improve the separability decision region for the classification task. Conclusions: The proposed method can be used as a reference to support future studies focusing on characterizing EEG signals oriented to the imagination of left and right hand movement, considering that our results proved to compared favourably to those reported in the literature. Language: Spanish.http://revistas.udistrital.edu.co/ojs/index.php/reving/article/view/10968BCIEEGWaveletPacket |
| spellingShingle | Boris Alexander Medina Ramón Alvarez López Characterization of EEG Signals Using Wavelet Packet and Fuzzy Entropy in Motor Imagination Tasks Ingeniería BCI EEG WaveletPacket |
| title | Characterization of EEG Signals Using Wavelet Packet and Fuzzy Entropy in Motor Imagination Tasks |
| title_full | Characterization of EEG Signals Using Wavelet Packet and Fuzzy Entropy in Motor Imagination Tasks |
| title_fullStr | Characterization of EEG Signals Using Wavelet Packet and Fuzzy Entropy in Motor Imagination Tasks |
| title_full_unstemmed | Characterization of EEG Signals Using Wavelet Packet and Fuzzy Entropy in Motor Imagination Tasks |
| title_short | Characterization of EEG Signals Using Wavelet Packet and Fuzzy Entropy in Motor Imagination Tasks |
| title_sort | characterization of eeg signals using wavelet packet and fuzzy entropy in motor imagination tasks |
| topic | BCI EEG WaveletPacket |
| url | http://revistas.udistrital.edu.co/ojs/index.php/reving/article/view/10968 |
| work_keys_str_mv | AT borisalexandermedina characterizationofeegsignalsusingwaveletpacketandfuzzyentropyinmotorimaginationtasks AT ramonalvarezlopez characterizationofeegsignalsusingwaveletpacketandfuzzyentropyinmotorimaginationtasks |