Variational bayes for spatiotemporal identification of event-related potential subcomponents
We propose a novel method for detection and tracking of event-related potential (ERP) subcomponents. The ERP subcomponent sources are assumed to be electric current dipoles (ECDs), and their locations and parameters (amplitude, latency, andwidth) are estimated and tracked fromtrial to trial.Variatio...
| Glavni autori: | , , , |
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| Format: | Journal article |
| Jezik: | English |
| Izdano: |
2010
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| _version_ | 1826275935477825536 |
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| author | Mohseni, H Ghaderi, F Wilding, E Sanei, S |
| author_facet | Mohseni, H Ghaderi, F Wilding, E Sanei, S |
| author_sort | Mohseni, H |
| collection | OXFORD |
| description | We propose a novel method for detection and tracking of event-related potential (ERP) subcomponents. The ERP subcomponent sources are assumed to be electric current dipoles (ECDs), and their locations and parameters (amplitude, latency, andwidth) are estimated and tracked fromtrial to trial.Variational Bayes implies that the parameters can be estimated separately using the likelihood function of each parameter. Estimations of ECD locations, which have nonlinear relations to the measurement, are obtained by particle filtering. Estimations of the amplitude and noise covariance matrix of the measurement are optimally given by the maximum likelihood (ML) approach, while estimations of the latency and the width are obtained by the Newton-Raphson technique. New recursive methods are introduced for both the ML and Newton-Raphson approaches to prevent divergence in the filtering procedure where there is a very low SNR. The main advantage of the method is the ability to track varying ECD locations. The proposed method is assessed using simulated as well as real data, and the results emphasize the potential of this new approach for the analysis of real-time measures of neural activity. © 2010 IEEE. |
| first_indexed | 2024-03-06T23:06:23Z |
| format | Journal article |
| id | oxford-uuid:63f4dd56-1f8d-404b-acf9-55f68be24d41 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:06:23Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | oxford-uuid:63f4dd56-1f8d-404b-acf9-55f68be24d412022-03-26T18:16:05ZVariational bayes for spatiotemporal identification of event-related potential subcomponentsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:63f4dd56-1f8d-404b-acf9-55f68be24d41EnglishSymplectic Elements at Oxford2010Mohseni, HGhaderi, FWilding, ESanei, SWe propose a novel method for detection and tracking of event-related potential (ERP) subcomponents. The ERP subcomponent sources are assumed to be electric current dipoles (ECDs), and their locations and parameters (amplitude, latency, andwidth) are estimated and tracked fromtrial to trial.Variational Bayes implies that the parameters can be estimated separately using the likelihood function of each parameter. Estimations of ECD locations, which have nonlinear relations to the measurement, are obtained by particle filtering. Estimations of the amplitude and noise covariance matrix of the measurement are optimally given by the maximum likelihood (ML) approach, while estimations of the latency and the width are obtained by the Newton-Raphson technique. New recursive methods are introduced for both the ML and Newton-Raphson approaches to prevent divergence in the filtering procedure where there is a very low SNR. The main advantage of the method is the ability to track varying ECD locations. The proposed method is assessed using simulated as well as real data, and the results emphasize the potential of this new approach for the analysis of real-time measures of neural activity. © 2010 IEEE. |
| spellingShingle | Mohseni, H Ghaderi, F Wilding, E Sanei, S Variational bayes for spatiotemporal identification of event-related potential subcomponents |
| title | Variational bayes for spatiotemporal identification of event-related potential subcomponents |
| title_full | Variational bayes for spatiotemporal identification of event-related potential subcomponents |
| title_fullStr | Variational bayes for spatiotemporal identification of event-related potential subcomponents |
| title_full_unstemmed | Variational bayes for spatiotemporal identification of event-related potential subcomponents |
| title_short | Variational bayes for spatiotemporal identification of event-related potential subcomponents |
| title_sort | variational bayes for spatiotemporal identification of event related potential subcomponents |
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