Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization
EEG source localization is determining possible cortical sources of brain activities with scalp EEG. Generally, every step of the data processing sequence affects the accuracy of EEG source localization. In this paper, we introduce a fused multivariate empirical mode decomposing (MEMD) and inverse s...
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| Format: | Article |
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De Gruyter
2018
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| author | Khosropanah, Pegah Ramli, Abdul Rahman Lim, Kheng Seang Marhaban, Mohammad Hamiruce Ahmedov, Anvarjon |
| author_facet | Khosropanah, Pegah Ramli, Abdul Rahman Lim, Kheng Seang Marhaban, Mohammad Hamiruce Ahmedov, Anvarjon |
| author_sort | Khosropanah, Pegah |
| collection | UM |
| description | EEG source localization is determining possible cortical sources of brain activities with scalp EEG. Generally, every step of the data processing sequence affects the accuracy of EEG source localization. In this paper, we introduce a fused multivariate empirical mode decomposing (MEMD) and inverse solution algorithm with an embedded unsupervised eye blink remover in order to localize the epileptogenic zone accurately. For this purpose, we constructed realistic forward models using MRI and boundary element method (BEM) for each patient to obtain results that are more realistic. We also developed an unsupervised algorithm utilizing a wavelet method to remove eye blink artifacts. Additionally, we applied MEMD, which is one of the recent and suitable feature extraction methods for non-linear, non-stationary, and multivariate signals such as EEG, to extract the signal of interest. We examined the localization results using the two most reliable linear distributed inverse methods in the literature: weighted minimum norm estimation (wMN) and standardized low resolution tomography (sLORETA). Results affirm the success of the proposed algorithm with the highest agreement compared to MRI reference by a specialist. Fusion of MEMD and sLORETA results in approximately zero localization error in terms of spatial difference with the validated MRI reference. High accuracy results of proposed algorithm using non-invasive and low-resolution EEG provide the potential of using this work for pre-surgical evaluation towards epileptogenic zone localization in clinics. |
| first_indexed | 2024-03-06T05:54:35Z |
| format | Article |
| id | um.eprints-21604 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:54:35Z |
| publishDate | 2018 |
| publisher | De Gruyter |
| record_format | dspace |
| spelling | um.eprints-216042019-07-15T03:46:35Z http://eprints.um.edu.my/21604/ Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization Khosropanah, Pegah Ramli, Abdul Rahman Lim, Kheng Seang Marhaban, Mohammad Hamiruce Ahmedov, Anvarjon R Medicine EEG source localization is determining possible cortical sources of brain activities with scalp EEG. Generally, every step of the data processing sequence affects the accuracy of EEG source localization. In this paper, we introduce a fused multivariate empirical mode decomposing (MEMD) and inverse solution algorithm with an embedded unsupervised eye blink remover in order to localize the epileptogenic zone accurately. For this purpose, we constructed realistic forward models using MRI and boundary element method (BEM) for each patient to obtain results that are more realistic. We also developed an unsupervised algorithm utilizing a wavelet method to remove eye blink artifacts. Additionally, we applied MEMD, which is one of the recent and suitable feature extraction methods for non-linear, non-stationary, and multivariate signals such as EEG, to extract the signal of interest. We examined the localization results using the two most reliable linear distributed inverse methods in the literature: weighted minimum norm estimation (wMN) and standardized low resolution tomography (sLORETA). Results affirm the success of the proposed algorithm with the highest agreement compared to MRI reference by a specialist. Fusion of MEMD and sLORETA results in approximately zero localization error in terms of spatial difference with the validated MRI reference. High accuracy results of proposed algorithm using non-invasive and low-resolution EEG provide the potential of using this work for pre-surgical evaluation towards epileptogenic zone localization in clinics. De Gruyter 2018 Article PeerReviewed Khosropanah, Pegah and Ramli, Abdul Rahman and Lim, Kheng Seang and Marhaban, Mohammad Hamiruce and Ahmedov, Anvarjon (2018) Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization. Biomedical Engineering / Biomedizinische Technik, 63 (4). pp. 467-479. ISSN 0013-5585, DOI https://doi.org/10.1515/bmt-2017-0011 <https://doi.org/10.1515/bmt-2017-0011>. https://doi.org/10.1515/bmt-2017-0011 doi:10.1515/bmt-2017-0011 |
| spellingShingle | R Medicine Khosropanah, Pegah Ramli, Abdul Rahman Lim, Kheng Seang Marhaban, Mohammad Hamiruce Ahmedov, Anvarjon Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization |
| title | Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization |
| title_full | Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization |
| title_fullStr | Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization |
| title_full_unstemmed | Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization |
| title_short | Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization |
| title_sort | fused multivariate empirical mode decomposition memd and inverse solution method for eeg source localization |
| topic | R Medicine |
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