Localization of Active Brain Sources From EEG Signals Using Empirical Mode Decomposition: A Comparative Study
The localization of active brain sources from Electroencephalogram (EEG) is a useful method in clinical applications, such as the study of localized epilepsy, evoked-related-potentials, and attention deficit/hyperactivity disorder. The distributed-source model is a common method to estimate neural a...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-11-01
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| Series: | Frontiers in Integrative Neuroscience |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fnint.2018.00055/full |
| _version_ | 1818969408015433728 |
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| author | Pablo Andrés Muñoz-Gutiérrez Pablo Andrés Muñoz-Gutiérrez Eduardo Giraldo Maximiliano Bueno-López Marta Molinas |
| author_facet | Pablo Andrés Muñoz-Gutiérrez Pablo Andrés Muñoz-Gutiérrez Eduardo Giraldo Maximiliano Bueno-López Marta Molinas |
| author_sort | Pablo Andrés Muñoz-Gutiérrez |
| collection | DOAJ |
| description | The localization of active brain sources from Electroencephalogram (EEG) is a useful method in clinical applications, such as the study of localized epilepsy, evoked-related-potentials, and attention deficit/hyperactivity disorder. The distributed-source model is a common method to estimate neural activity in the brain. The location and amplitude of each active source are estimated by solving the inverse problem by regularization or using Bayesian methods with spatio-temporal constraints. Frequency and spatio-temporal constraints improve the quality of the reconstructed neural activity. However, separation into frequency bands is beneficial when the relevant information is in specific sub-bands. We improved frequency-band identification and preserved good temporal resolution using EEG pre-processing techniques with good frequency band separation and temporal resolution properties. The identified frequency bands were included as constraints in the solution of the inverse problem by decomposing the EEG signals into frequency bands through various methods that offer good frequency and temporal resolution, such as empirical mode decomposition (EMD) and wavelet transform (WT). We present a comparative analysis of the accuracy of brain-source reconstruction using these techniques. The accuracy of the spatial reconstruction was assessed using the Wasserstein metric for real and simulated signals. We approached the mode-mixing problem, inherent to EMD, by exploring three variants of EMD: masking EMD, Ensemble-EMD (EEMD), and multivariate EMD (MEMD). The results of the spatio-temporal brain source reconstruction using these techniques show that masking EMD and MEMD can largely mitigate the mode-mixing problem and achieve a good spatio-temporal reconstruction of the active sources. Masking EMD and EEMD achieved better reconstruction than standard EMD, Multiple Sparse Priors, or wavelet packet decomposition when EMD was used as a pre-processing tool for the spatial reconstruction (averaged over time) of the brain sources. The spatial resolution obtained using all three EMD variants was substantially better than the use of EMD alone, as the mode-mixing problem was mitigated, particularly with masking EMD and EEMD. These findings encourage further exploration into the use of EMD-based pre-processing, the mode-mixing problem, and its impact on the accuracy of brain source activity reconstruction. |
| first_indexed | 2024-12-20T14:20:06Z |
| format | Article |
| id | doaj.art-7a6f9e8c8a934858b62d8da63ecc6469 |
| institution | Directory Open Access Journal |
| issn | 1662-5145 |
| language | English |
| last_indexed | 2024-12-20T14:20:06Z |
| publishDate | 2018-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Integrative Neuroscience |
| spelling | doaj.art-7a6f9e8c8a934858b62d8da63ecc64692022-12-21T19:37:57ZengFrontiers Media S.A.Frontiers in Integrative Neuroscience1662-51452018-11-011210.3389/fnint.2018.00055409377Localization of Active Brain Sources From EEG Signals Using Empirical Mode Decomposition: A Comparative StudyPablo Andrés Muñoz-Gutiérrez0Pablo Andrés Muñoz-Gutiérrez1Eduardo Giraldo2Maximiliano Bueno-López3Marta Molinas4Electronic Instrumentation Technology, Universidad del Quindío, Armenia, ColombiaDepartment of Electrical Engineering, Universidad Tecnológica de Pereira, Pereira, ColombiaDepartment of Electrical Engineering, Universidad Tecnológica de Pereira, Pereira, ColombiaDepartment of Electrical Engineering, Universidad de La Salle, Bogotá, ColombiaDepartment of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, NorwayThe localization of active brain sources from Electroencephalogram (EEG) is a useful method in clinical applications, such as the study of localized epilepsy, evoked-related-potentials, and attention deficit/hyperactivity disorder. The distributed-source model is a common method to estimate neural activity in the brain. The location and amplitude of each active source are estimated by solving the inverse problem by regularization or using Bayesian methods with spatio-temporal constraints. Frequency and spatio-temporal constraints improve the quality of the reconstructed neural activity. However, separation into frequency bands is beneficial when the relevant information is in specific sub-bands. We improved frequency-band identification and preserved good temporal resolution using EEG pre-processing techniques with good frequency band separation and temporal resolution properties. The identified frequency bands were included as constraints in the solution of the inverse problem by decomposing the EEG signals into frequency bands through various methods that offer good frequency and temporal resolution, such as empirical mode decomposition (EMD) and wavelet transform (WT). We present a comparative analysis of the accuracy of brain-source reconstruction using these techniques. The accuracy of the spatial reconstruction was assessed using the Wasserstein metric for real and simulated signals. We approached the mode-mixing problem, inherent to EMD, by exploring three variants of EMD: masking EMD, Ensemble-EMD (EEMD), and multivariate EMD (MEMD). The results of the spatio-temporal brain source reconstruction using these techniques show that masking EMD and MEMD can largely mitigate the mode-mixing problem and achieve a good spatio-temporal reconstruction of the active sources. Masking EMD and EEMD achieved better reconstruction than standard EMD, Multiple Sparse Priors, or wavelet packet decomposition when EMD was used as a pre-processing tool for the spatial reconstruction (averaged over time) of the brain sources. The spatial resolution obtained using all three EMD variants was substantially better than the use of EMD alone, as the mode-mixing problem was mitigated, particularly with masking EMD and EEMD. These findings encourage further exploration into the use of EMD-based pre-processing, the mode-mixing problem, and its impact on the accuracy of brain source activity reconstruction.https://www.frontiersin.org/article/10.3389/fnint.2018.00055/fullbrain mappingdenoisingEEG signalsfrequency detectionempirical mode decomposition |
| spellingShingle | Pablo Andrés Muñoz-Gutiérrez Pablo Andrés Muñoz-Gutiérrez Eduardo Giraldo Maximiliano Bueno-López Marta Molinas Localization of Active Brain Sources From EEG Signals Using Empirical Mode Decomposition: A Comparative Study Frontiers in Integrative Neuroscience brain mapping denoising EEG signals frequency detection empirical mode decomposition |
| title | Localization of Active Brain Sources From EEG Signals Using Empirical Mode Decomposition: A Comparative Study |
| title_full | Localization of Active Brain Sources From EEG Signals Using Empirical Mode Decomposition: A Comparative Study |
| title_fullStr | Localization of Active Brain Sources From EEG Signals Using Empirical Mode Decomposition: A Comparative Study |
| title_full_unstemmed | Localization of Active Brain Sources From EEG Signals Using Empirical Mode Decomposition: A Comparative Study |
| title_short | Localization of Active Brain Sources From EEG Signals Using Empirical Mode Decomposition: A Comparative Study |
| title_sort | localization of active brain sources from eeg signals using empirical mode decomposition a comparative study |
| topic | brain mapping denoising EEG signals frequency detection empirical mode decomposition |
| url | https://www.frontiersin.org/article/10.3389/fnint.2018.00055/full |
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