Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball Tasks
Neural responses of oddball tasks can be used as a physiological biomarker to evaluate the brain potential of information processing under the assumption that the differential contribution of deviant stimuli can be assessed accurately. Nevertheless, the non-stationarity of neural activity causes the...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-05-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fnins.2020.00446/full |
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| author | Jorge I. Padilla-Buritica Jorge I. Padilla-Buritica Jorge I. Padilla-Buritica Jose M. Ferrandez-Vicente German A. Castaño Carlos D. Acosta-Medina |
| author_facet | Jorge I. Padilla-Buritica Jorge I. Padilla-Buritica Jorge I. Padilla-Buritica Jose M. Ferrandez-Vicente German A. Castaño Carlos D. Acosta-Medina |
| author_sort | Jorge I. Padilla-Buritica |
| collection | DOAJ |
| description | Neural responses of oddball tasks can be used as a physiological biomarker to evaluate the brain potential of information processing under the assumption that the differential contribution of deviant stimuli can be assessed accurately. Nevertheless, the non-stationarity of neural activity causes the brain networks to fluctuate hugely in time, deteriorating the estimation of pairwise synergies. To deal with the time variability of neural responses, we have developed a piecewise multi-subject analysis that is applied over a set of time intervals within the stationary assumption holds. To segment the whole stimulus-locked epoch into multiple temporal windows, we experimented with two approaches for piecewise segmentation of EEG recordings: a fixed time-window, at which the estimates of FC measures fulfill a given confidence level, and variable time-window, which is segmented at the change points of the time-varying classifier performance. Employing the weighted Phase Lock Index as a functional connectivity metric, we have presented the validation in a real-world EEG data, proving the effectiveness of variable time segmentation for connectivity extraction when combined with a supervised thresholding approach. Consequently, we performed a piecewise group-level analysis of electroencephalographic data that deals with non-stationary functional connectivity measures, evaluating more carefully the contribution of a link node-set in discriminating between the labeled oddball responses. |
| first_indexed | 2024-12-23T10:37:28Z |
| format | Article |
| id | doaj.art-241615fdd8e5485d8cf6ef30ca274258 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-23T10:37:28Z |
| publishDate | 2020-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-241615fdd8e5485d8cf6ef30ca2742582022-12-21T17:50:15ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2020-05-011410.3389/fnins.2020.00446500787Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball TasksJorge I. Padilla-Buritica0Jorge I. Padilla-Buritica1Jorge I. Padilla-Buritica2Jose M. Ferrandez-Vicente3German A. Castaño4Carlos D. Acosta-Medina5Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales, ColombiaDiseño Electrónico y Técnicas de Tratamiento de Señales, Universidad Politécnica de Cartagena, Cartagena, SpainGrupo de Automática, Universidad Autónoma, Manizales, ColombiaDiseño Electrónico y Técnicas de Tratamiento de Señales, Universidad Politécnica de Cartagena, Cartagena, SpainGrupo de Trabajo Academico Cultura de la Calidad en la Educacion, Universidad Nacional de Colombia, Manizales, ColombiaSignal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales, ColombiaNeural responses of oddball tasks can be used as a physiological biomarker to evaluate the brain potential of information processing under the assumption that the differential contribution of deviant stimuli can be assessed accurately. Nevertheless, the non-stationarity of neural activity causes the brain networks to fluctuate hugely in time, deteriorating the estimation of pairwise synergies. To deal with the time variability of neural responses, we have developed a piecewise multi-subject analysis that is applied over a set of time intervals within the stationary assumption holds. To segment the whole stimulus-locked epoch into multiple temporal windows, we experimented with two approaches for piecewise segmentation of EEG recordings: a fixed time-window, at which the estimates of FC measures fulfill a given confidence level, and variable time-window, which is segmented at the change points of the time-varying classifier performance. Employing the weighted Phase Lock Index as a functional connectivity metric, we have presented the validation in a real-world EEG data, proving the effectiveness of variable time segmentation for connectivity extraction when combined with a supervised thresholding approach. Consequently, we performed a piecewise group-level analysis of electroencephalographic data that deals with non-stationary functional connectivity measures, evaluating more carefully the contribution of a link node-set in discriminating between the labeled oddball responses.https://www.frontiersin.org/article/10.3389/fnins.2020.00446/fullbrain connectivityWPLIoddball paradigmnon-stationarygroup analysisEEG |
| spellingShingle | Jorge I. Padilla-Buritica Jorge I. Padilla-Buritica Jorge I. Padilla-Buritica Jose M. Ferrandez-Vicente German A. Castaño Carlos D. Acosta-Medina Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball Tasks Frontiers in Neuroscience brain connectivity WPLI oddball paradigm non-stationary group analysis EEG |
| title | Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball Tasks |
| title_full | Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball Tasks |
| title_fullStr | Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball Tasks |
| title_full_unstemmed | Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball Tasks |
| title_short | Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball Tasks |
| title_sort | non stationary group level connectivity analysis for enhanced interpretability of oddball tasks |
| topic | brain connectivity WPLI oddball paradigm non-stationary group analysis EEG |
| url | https://www.frontiersin.org/article/10.3389/fnins.2020.00446/full |
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