Using Data Assimilation for Quantitative Electroencephalography Analysis
We propose a method based on the ensemble Kalman filter (EnKF) together with quantitative electroencephalogram (QEEG) coherence and power spectrum analysis for evaluating changes in brain activity associated with cognitive processes. Such analysis framework has been widely used in the context of dat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/10/11/853 |
| _version_ | 1827702330924990464 |
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| author | Lizbeth Peralta-Malváez Rocio Salazar-Varas Gibran Etcheverry David Gutiérrez |
| author_facet | Lizbeth Peralta-Malváez Rocio Salazar-Varas Gibran Etcheverry David Gutiérrez |
| author_sort | Lizbeth Peralta-Malváez |
| collection | DOAJ |
| description | We propose a method based on the ensemble Kalman filter (EnKF) together with quantitative electroencephalogram (QEEG) coherence and power spectrum analysis for evaluating changes in brain activity associated with cognitive processes. Such analysis framework has been widely used in the context of data assimilation (DA) in areas such as geosciences, meteorology, and aerospace. However, the use of this approach is less common in neurosciences. In our case, EnKF highlights the spectral contribution of brain signals that are more likely (according to their coherence analysis) to be related to the cognitive process of interest. The power enhancement, due to the cognitive activity, is later validated in the power spectrum analysis by comparing through statistical tests relevant frequency content in two datasets in which assessing the development of cognitive abilities is of interest: the process of getting concentrated and of learning a new skill. Our results show that our DA-based methodology can highlight important frequency characteristics of the electroencephalogram (EEG) data that have been related to different cognitive processes. Hence, our proposal has the potential to understand of neurocognitive phenomena that is tracked through QEEG. |
| first_indexed | 2024-03-10T14:53:53Z |
| format | Article |
| id | doaj.art-672dcfe771fb4f358d4badad3182fd45 |
| institution | Directory Open Access Journal |
| issn | 2076-3425 |
| language | English |
| last_indexed | 2024-03-10T14:53:53Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj.art-672dcfe771fb4f358d4badad3182fd452023-11-20T20:47:15ZengMDPI AGBrain Sciences2076-34252020-11-01101185310.3390/brainsci10110853Using Data Assimilation for Quantitative Electroencephalography AnalysisLizbeth Peralta-Malváez0Rocio Salazar-Varas1Gibran Etcheverry2David Gutiérrez3Department of Computing, Electronics and Mechatronics, Universidad de las Américas Puebla, San Andrés Cholula, Puebla 72810, MexicoDepartment of Computing, Electronics and Mechatronics, Universidad de las Américas Puebla, San Andrés Cholula, Puebla 72810, MexicoDepartment of Computing, Electronics and Mechatronics, Universidad de las Américas Puebla, San Andrés Cholula, Puebla 72810, MexicoCenter for Research and Advanced Studies (Cinvestav), Monterrey’s Unit Apodaca, Nuevo León 66600, MexicoWe propose a method based on the ensemble Kalman filter (EnKF) together with quantitative electroencephalogram (QEEG) coherence and power spectrum analysis for evaluating changes in brain activity associated with cognitive processes. Such analysis framework has been widely used in the context of data assimilation (DA) in areas such as geosciences, meteorology, and aerospace. However, the use of this approach is less common in neurosciences. In our case, EnKF highlights the spectral contribution of brain signals that are more likely (according to their coherence analysis) to be related to the cognitive process of interest. The power enhancement, due to the cognitive activity, is later validated in the power spectrum analysis by comparing through statistical tests relevant frequency content in two datasets in which assessing the development of cognitive abilities is of interest: the process of getting concentrated and of learning a new skill. Our results show that our DA-based methodology can highlight important frequency characteristics of the electroencephalogram (EEG) data that have been related to different cognitive processes. Hence, our proposal has the potential to understand of neurocognitive phenomena that is tracked through QEEG.https://www.mdpi.com/2076-3425/10/11/853data assimilationquantitative electroencephalographyEnsemble Kalman filterneurocognitive processes |
| spellingShingle | Lizbeth Peralta-Malváez Rocio Salazar-Varas Gibran Etcheverry David Gutiérrez Using Data Assimilation for Quantitative Electroencephalography Analysis Brain Sciences data assimilation quantitative electroencephalography Ensemble Kalman filter neurocognitive processes |
| title | Using Data Assimilation for Quantitative Electroencephalography Analysis |
| title_full | Using Data Assimilation for Quantitative Electroencephalography Analysis |
| title_fullStr | Using Data Assimilation for Quantitative Electroencephalography Analysis |
| title_full_unstemmed | Using Data Assimilation for Quantitative Electroencephalography Analysis |
| title_short | Using Data Assimilation for Quantitative Electroencephalography Analysis |
| title_sort | using data assimilation for quantitative electroencephalography analysis |
| topic | data assimilation quantitative electroencephalography Ensemble Kalman filter neurocognitive processes |
| url | https://www.mdpi.com/2076-3425/10/11/853 |
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