Scalable spatio-temporal Bayesian analysis of high-dimensional electroencephalography data
We present a scalable Bayesian modelling approach for identifying brain regions that respond to a certain stimulus and use them to classify subjects. More specifically, we deal with multi-subject electroencephalography (EEG) data with a binary response distinguishing between alcoholic and control gr...
Gespeichert in:
| Veröffentlicht in: | Canadian journal of statistics Jg. 49; H. 1; S. 107 - 128 |
|---|---|
| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Hoboken, USA
Wiley
01.03.2021
John Wiley & Sons, Inc Wiley Subscription Services, Inc |
| Schlagworte: | |
| ISSN: | 0319-5724, 1708-945X |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | We present a scalable Bayesian modelling approach for identifying brain regions that respond to a certain stimulus and use them to classify subjects. More specifically, we deal with multi-subject electroencephalography (EEG) data with a binary response distinguishing between alcoholic and control groups. The covariates are matrix-variate with measurements taken from each subject at different locations across multiple time points. EEG data have a complex structure with both spatial and temporal attributes. We use a divide-and-conquer strategy and build separate local models, that is, one model at each time point. We employ Bayesian variable selection approaches using a structured continuous spike-and-slab prior to identify the locations that respond to a certain stimulus. We incorporate the spatio-temporal structure through a Kronecker product of the spatial and temporal correlation matrices. We develop a highly scalable estimation algorithm, using likelihood approximation, to deal with large number of parameters in the model. Variable selection is done via clustering of the locations based on their duration of activation. We use scoring rules to evaluate the prediction performance. Simulation studies demonstrate the efficiency of our scalable algorithm in terms of estimation and fast computation. We present results using our scalable approach on a case study of multi-subject EEG data.
Les auteurs présentent une approche de modélisation bayésienne évolutive pour l’identification des régions du cerveau qui répondent à un certain stimulus afin de les utiliser pour classer les sujets. Ils travaillent nommément avec des données d’électroencéphalogramme (EEG) multi-sujets où une variable binaire distingue les sujets alcooliques des individus du groupe contrôle. Les covariables sont des matrices de mesures prises sur chaque sujet à différents endroits et différents moments. Les données d’EEG ont une structure complexe comportant des attributs spatiaux et temporels. Les auteurs adoptent une stratégie de type diviser pour régner et construisent des modèles locaux, à raison d’un modèle pour chaque point temporel. Ils mettent de l’avant une approche bayésienne de sélection de variables exploitant une loi a priori continue en pic et plateau afin d’identifier la localisation d’un certain stimulus. Ils incorporent la structure spatio-temporelle à l’aide d’un produit de Kronecker des matrices de corrélation spatiales et temporelles. Ils développent un algorithme d’estimation hautement évolutif misant sur une approximation de la vraisemblance afin de gérer le grand nombre de paramètres dans le modèle. Une sélection de variables est faite en regroupant les lieux selon la durée de leur activation. Les auteurs exploitent des règles de pointage afin d’évaluer la performance en termes de prévisions. Ils démontrent par des études de simulation l’efficacité de leur algorithme évolutif et en termes d’estimation et de rapidité des calculs. Ils présentent les résultats obtenus avec leur méthode évolutive pour une étude de cas avec des données EEG multi-sujets. |
|---|---|
| AbstractList | We present a scalable Bayesian modelling approach for identifying brain regions that respond to a certain stimulus and use them to classify subjects. More specifically, we deal with multi‐subject electroencephalography (EEG) data with a binary response distinguishing between alcoholic and control groups. The covariates are matrix‐variate with measurements taken from each subject at different locations across multiple time points. EEG data have a complex structure with both spatial and temporal attributes. We use a divide‐and‐conquer strategy and build separate local models, that is, one model at each time point. We employ Bayesian variable selection approaches using a structured continuous spike‐and‐slab prior to identify the locations that respond to a certain stimulus. We incorporate the spatio‐temporal structure through a Kronecker product of the spatial and temporal correlation matrices. We develop a highly scalable estimation algorithm, using likelihood approximation, to deal with large number of parameters in the model. Variable selection is done via clustering of the locations based on their duration of activation. We use scoring rules to evaluate the prediction performance. Simulation studies demonstrate the efficiency of our scalable algorithm in terms of estimation and fast computation. We present results using our scalable approach on a case study of multi‐subject EEG data.
Résumé
Les auteurs présentent une approche de modélisation bayésienne évolutive pour l'identification des régions du cerveau qui répondent à un certain stimulus afin de les utiliser pour classer les sujets. Ils travaillent nommément avec des données d'électroencéphalogramme (EEG) multi‐sujets où une variable binaire distingue les sujets alcooliques des individus du groupe contrôle. Les covariables sont des matrices de mesures prises sur chaque sujet à différents endroits et différents moments. Les données d'EEG ont une structure complexe comportant des attributs spatiaux et temporels. Les auteurs adoptent une stratégie de type diviser pour régner et construisent des modèles locaux, à raison d'un modèle pour chaque point temporel. Ils mettent de l'avant une approche bayésienne de sélection de variables exploitant une loi a priori continue en pic et plateau afin d'identifier la localisation d'un certain stimulus. Ils incorporent la structure spatio‐temporelle à l'aide d'un produit de Kronecker des matrices de corrélation spatiales et temporelles. Ils développent un algorithme d'estimation hautement évolutif misant sur une approximation de la vraisemblance afin de gérer le grand nombre de paramètres dans le modèle. Une sélection de variables est faite en regroupant les lieux selon la durée de leur activation. Les auteurs exploitent des règles de pointage afin d'évaluer la performance en termes de prévisions. Ils démontrent par des études de simulation l'efficacité de leur algorithme évolutif et en termes d'estimation et de rapidité des calculs. Ils présentent les résultats obtenus avec leur méthode évolutive pour une étude de cas avec des données EEG multi‐sujets. We present a scalable Bayesian modelling approach for identifying brain regions that respond to a certain stimulus and use them to classify subjects. More specifically, we deal with multi-subject electroencephalography (EEG) data with a binary response distinguishing between alcoholic and control groups. The covariates are matrix-variate with measurements taken from each subject at different locations across multiple time points. EEG data have a complex structure with both spatial and temporal attributes. We use a divide-and-conquer strategy and build separate local models, that is, one model at each time point. We employ Bayesian variable selection approaches using a structured continuous spike-and-slab prior to identify the locations that respond to a certain stimulus. We incorporate the spatio-temporal structure through a Kronecker product of the spatial and temporal correlation matrices. We develop a highly scalable estimation algorithm, using likelihood approximation, to deal with large number of parameters in the model. Variable selection is done via clustering of the locations based on their duration of activation. We use scoring rules to evaluate the prediction performance. Simulation studies demonstrate the efficiency of our scalable algorithm in terms of estimation and fast computation. We present results using our scalable approach on a case study of multi-subject EEG data. Les auteurs présentent une approche de modélisation bayésienne évolutive pour l’identification des régions du cerveau qui répondent à un certain stimulus afin de les utiliser pour classer les sujets. Ils travaillent nommément avec des données d’électroencéphalogramme (EEG) multi-sujets où une variable binaire distingue les sujets alcooliques des individus du groupe contrôle. Les covariables sont des matrices de mesures prises sur chaque sujet à différents endroits et différents moments. Les données d’EEG ont une structure complexe comportant des attributs spatiaux et temporels. Les auteurs adoptent une stratégie de type diviser pour régner et construisent des modèles locaux, à raison d’un modèle pour chaque point temporel. Ils mettent de l’avant une approche bayésienne de sélection de variables exploitant une loi a priori continue en pic et plateau afin d’identifier la localisation d’un certain stimulus. Ils incorporent la structure spatio-temporelle à l’aide d’un produit de Kronecker des matrices de corrélation spatiales et temporelles. Ils développent un algorithme d’estimation hautement évolutif misant sur une approximation de la vraisemblance afin de gérer le grand nombre de paramètres dans le modèle. Une sélection de variables est faite en regroupant les lieux selon la durée de leur activation. Les auteurs exploitent des règles de pointage afin d’évaluer la performance en termes de prévisions. Ils démontrent par des études de simulation l’efficacité de leur algorithme évolutif et en termes d’estimation et de rapidité des calculs. Ils présentent les résultats obtenus avec leur méthode évolutive pour une étude de cas avec des données EEG multi-sujets. We present a scalable Bayesian modelling approach for identifying brain regions that respond to a certain stimulus and use them to classify subjects. More specifically, we deal with multi‐subject electroencephalography (EEG) data with a binary response distinguishing between alcoholic and control groups. The covariates are matrix‐variate with measurements taken from each subject at different locations across multiple time points. EEG data have a complex structure with both spatial and temporal attributes. We use a divide‐and‐conquer strategy and build separate local models, that is, one model at each time point. We employ Bayesian variable selection approaches using a structured continuous spike‐and‐slab prior to identify the locations that respond to a certain stimulus. We incorporate the spatio‐temporal structure through a Kronecker product of the spatial and temporal correlation matrices. We develop a highly scalable estimation algorithm, using likelihood approximation, to deal with large number of parameters in the model. Variable selection is done via clustering of the locations based on their duration of activation. We use scoring rules to evaluate the prediction performance. Simulation studies demonstrate the efficiency of our scalable algorithm in terms of estimation and fast computation. We present results using our scalable approach on a case study of multi‐subject EEG data. |
| Author | DEY, Dipak K. MOHAMMED, Shariq |
| Author_xml | – sequence: 1 givenname: Shariq surname: MOHAMMED fullname: MOHAMMED, Shariq – sequence: 2 givenname: Dipak K. surname: DEY fullname: DEY, Dipak K. |
| BookMark | eNp1kE1Lw0AQQBdRsK0e_AFCwJOHtLuzyaZ71OInBQ_tQbwsk2TTJGyzcTdF8u-NRr15GhjeG4Y3JceNbTQhF4zOGaWwyGo_ZyyWcEQmLKHLUEbx6zGZUM5kGCcQnZKp9zWlPGYMJuRtk6HB1OjAt9hVNuz0vrUOTXCLvfYVNgE2aHpf-cAWQVntyjCv9rrxlR32gTY665zVTabbEo3dOWzLPsixwzNyUqDx-vxnzsj2_m67egzXLw9Pq5t1mEEiIQQKUZ6yXAguINIgKBMsK5hIBEedSpZgnAkuRVTQIscIIYWIyYIVOeQc-YxcjWdbZ98P2neqtgc3_OYVxFQCcODJQF2PVOas904XqnXVHl2vGFVf5dRQTn2XG9jFyH5URvf_g2r1vPk1Lkej9p11f0a0TAQsJeefR8d7vQ |
| Cites_doi | 10.1016/j.neuroimage.2008.02.059 10.1080/01621459.2013.776499 10.1186/1743-0003-5-25 10.1016/S0079-6123(06)59003-X 10.1111/rssc.12369 10.1080/00401706.1970.10488634 10.1111/rssb.12031 10.1198/016214506000001437 10.1007/978-981-13-0908-3 10.1016/0167-8760(84)90014-X 10.1080/01621459.2015.1044091 10.1080/01621459.1971.10482346 10.1111/biom.12126 10.1016/j.neuroimage.2007.09.048 10.1111/biom.12355 10.1002/sam.11477 10.1109/TSP.2013.2272287 10.1214/aos/1056562461 10.1037/0278-7393.6.2.174 10.1214/14-BA860 10.1016/j.jneumeth.2003.10.009 10.1093/biomet/57.1.97 10.1080/01621459.1988.10478694 10.1111/j.2517-6161.1988.tb01729.x 10.1006/nimg.2002.1143 10.1016/j.bspc.2014.01.009 10.1109/TMI.2017.2780185 10.1111/rssb.12154 10.1093/biomet/83.4.715 10.1214/16-AOAS931 10.1080/01621459.1993.10476321 10.1016/j.neuroimage.2007.07.062 10.1016/j.neuroimage.2016.08.064 10.1111/j.2517-6161.1996.tb02080.x 10.1109/JSTSP.2016.2600023 10.1214/11-AOAS531 10.1111/j.1467-9868.2008.00663.x |
| ContentType | Journal Article |
| Copyright | 2021 Statistical Society of Canada |
| Copyright_xml | – notice: 2021 Statistical Society of Canada |
| DBID | AAYXX CITATION 7SC 8BJ 8FD FQK H8D JBE JQ2 L7M L~C L~D |
| DOI | 10.1002/cjs.11592 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts International Bibliography of the Social Sciences (IBSS) Technology Research Database International Bibliography of the Social Sciences Aerospace Database International Bibliography of the Social Sciences ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Aerospace Database International Bibliography of the Social Sciences (IBSS) Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | CrossRef Aerospace Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Statistics Mathematics |
| EISSN | 1708-945X |
| EndPage | 128 |
| ExternalDocumentID | 10_1002_cjs_11592 CJS11592 48762893 |
| Genre | article |
| GroupedDBID | .3N .GA 05W 0R~ 123 1L6 1OC 29B 33P 3SF 3WU 4.4 44B 50Y 50Z 52M 52O 52T 52U 52W 6J9 702 7PT 8-0 8-1 8-3 8-4 8-5 930 A03 AAESR AAEVG AAHQN AAMMB AANLZ AAONW AAXRX AAYCA AAZKR ABBHK ABCUV ABDBF ABFAN ABJNI ABPVW ABQDR ABYWD ACAHQ ACCZN ACGFO ACGFS ACIWK ACMTB ACPOU ACTMH ACUHS ACXBN ACXQS ADBBV ADEOM ADIZJ ADKYN ADMGS ADODI ADZMN AEFGJ AEGXH AEIGN AEIMD AELLO AEUPB AEUYR AEYWJ AFBPY AFFPM AFGKR AFVYC AFWVQ AGXDD AGYGG AHBTC AIAGR AIDQK AIDYY AITYG AIURR AJXKR AKBRZ ALAGY ALMA_UNASSIGNED_HOLDINGS ALRMG ALUQN AMBMR AMVHM AMYDB ATUGU AUFTA AZBYB AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI CS3 D-E D-F DCZOG DPXWK DQDLB DRFUL DRSTM DSRWC EBS ECEWR ESX F00 F01 F04 F21 F5P G-S G.N GODZA H.T H.X HGLYW HQ6 HZ~ IPSME JAA JENOY JMS JPL JST LATKE LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LYRES MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 NF~ NNB O66 O9- P2P P2W P2X P4D PQQKQ PZZ Q.N QB0 QRW R.K ROL RX1 SA0 SUPJJ TN5 TUS UB1 W8V W99 WBKPD WIH WIK WOHZO WYISQ XBAML XV2 ZZTAW ~IA ~WT .Y3 3-9 31~ AANHP AASGY AAWIL ABAWQ ABXSQ ACBWZ ACDIW ACHJO ACRPL ACYXJ ADNMO ADULT AGLNM AGQPQ AI. AIHAF ASPBG AVWKF AZFZN EJD FEDTE FSPIC GIFXF HF~ HGD HVGLF JAAYA JBMMH JBZCM JHFFW JKQEH JLEZI JLXEF LW6 PALCI RIWAO RJQFR RNS VH1 AAYXX AIQQE CITATION O8X 7SC 8BJ 8FD FQK H8D JBE JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c2792-2024db1d663624e260161cf16763aeb917a5c63964f0fda4a2b2419f1fd2d3a3 |
| IEDL.DBID | DRFUL |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000617161100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0319-5724 |
| IngestDate | Mon Nov 10 00:42:59 EST 2025 Sat Nov 29 01:41:54 EST 2025 Wed Aug 20 07:26:01 EDT 2025 Thu Jul 03 21:28:49 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c2792-2024db1d663624e260161cf16763aeb917a5c63964f0fda4a2b2419f1fd2d3a3 |
| Notes | NOTE: This article has supplementary material for online publication. NOTE: This article is intended for the special issue on Neuroimaging and must be held for that issue. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-3107-2969 0000-0002-9367-9731 |
| PQID | 2509223237 |
| PQPubID | 46823 |
| PageCount | 22 |
| ParticipantIDs | proquest_journals_2509223237 crossref_primary_10_1002_cjs_11592 wiley_primary_10_1002_cjs_11592_CJS11592 jstor_primary_48762893 |
| PublicationCentury | 2000 |
| PublicationDate | 20210301 March 2021 2021-03-00 |
| PublicationDateYYYYMMDD | 2021-03-01 |
| PublicationDate_xml | – month: 3 year: 2021 text: 20210301 day: 1 |
| PublicationDecade | 2020 |
| PublicationPlace | Hoboken, USA |
| PublicationPlace_xml | – name: Hoboken, USA – name: Ottawa |
| PublicationTitle | Canadian journal of statistics |
| PublicationYear | 2021 |
| Publisher | Wiley John Wiley & Sons, Inc Wiley Subscription Services, Inc |
| Publisher_xml | – name: Wiley – name: John Wiley & Sons, Inc – name: Wiley Subscription Services, Inc |
| References | 2002; 16 2009; 44 1971; 66 2007; 102 2014; 70 2015; 71 2013; 108 1993; 88 2016b; 111 2013; 61 2008; 39 1970; 12 2016; 10 2008; 5 2020; 13 1988; 50 1996; 58 2006; 159 2008; 70 2003; 31 2016; 78 1970; 57 2004; 134 2013; 14 2017; 37 2019; 68 1996; 83 1980; 6 2018 1988; 83 2011; 24 2017; 18 2014 1994; 18 2017; 144 2014; 9 2012; 6 2016a; 10 2014; 11 2014; 76 e_1_2_8_28_1 Andersen M. R. (e_1_2_8_3_1) 2014 e_1_2_8_29_1 e_1_2_8_24_1 e_1_2_8_25_1 e_1_2_8_26_1 e_1_2_8_27_1 Andersen M. R. (e_1_2_8_4_1) 2017; 18 Hernández‐Lobato D. (e_1_2_8_16_1) 2013; 14 e_1_2_8_2_1 e_1_2_8_5_1 e_1_2_8_7_1 e_1_2_8_6_1 e_1_2_8_9_1 e_1_2_8_8_1 e_1_2_8_20_1 Vecchia A. V. (e_1_2_8_37_1) 1988; 50 e_1_2_8_21_1 e_1_2_8_42_1 e_1_2_8_22_1 e_1_2_8_23_1 e_1_2_8_41_1 e_1_2_8_40_1 e_1_2_8_17_1 e_1_2_8_18_1 e_1_2_8_39_1 e_1_2_8_19_1 e_1_2_8_13_1 e_1_2_8_36_1 e_1_2_8_14_1 e_1_2_8_15_1 e_1_2_8_38_1 Tibshirani R. (e_1_2_8_33_1) 1996; 58 e_1_2_8_32_1 e_1_2_8_10_1 e_1_2_8_31_1 e_1_2_8_11_1 e_1_2_8_34_1 Titsias M. K. (e_1_2_8_35_1) 2011 e_1_2_8_12_1 e_1_2_8_30_1 |
| References_xml | – volume: 57 start-page: 97 year: 1970 end-page: 109 article-title: Monte Carlo sampling methods using Markov chains and their applications publication-title: Biometrika – volume: 6 start-page: 174 year: 1980 article-title: A standardized set of 260 pictures: Norms for name agreement, image agreement, familiarity, and visual complexity publication-title: Journal of Experimental Psychology: Human Learning and Memory – volume: 10 start-page: 1315 issue: 7 year: 2016 end-page: 1325 article-title: Modeling effective connectivity in high‐dimensional cortical source signals publication-title: IEEE Journal of Selected Topics in Signal Processing – volume: 88 start-page: 669 year: 1993 end-page: 679 article-title: Bayesian analysis of binary and polychotomous response data publication-title: Journal of the American Statistical Association – volume: 111 start-page: 800 year: 2016b end-page: 812 article-title: Hierarchical nearest‐neighbor Gaussian process models for large geostatistical datasets publication-title: Journal of the American Statistical Association – volume: 71 start-page: 905 year: 2015 end-page: 917 article-title: Local‐aggregate modeling for big data via distributed optimization: Applications to neuroimaging publication-title: Biometrics – volume: 11 start-page: 42 year: 2014 end-page: 52 article-title: A survey of methods used for source localization using EEG signals publication-title: Biomedical Signal Processing and Control – volume: 6 start-page: 1021 year: 2012 end-page: 1046 article-title: A two‐way regularization method for MEG source reconstruction publication-title: The Annals of Applied Statistics – volume: 70 start-page: 132 year: 2014 end-page: 143 article-title: A variational Bayes spatiotemporal model for electromagnetic brain mapping publication-title: Biometrics – volume: 5 start-page: 25 year: 2008 article-title: Review on solving the inverse problem in EEG source analysis publication-title: Journal of Neuroengineering and Rehabilitation – volume: 134 start-page: 9 year: 2004 end-page: 21 article-title: EEGLAB: An open source toolbox for analysis of single‐trial EEG dynamics including independent component analysis publication-title: Journal of Neuroscience Methods – volume: 83 start-page: 715 year: 1996 end-page: 726 article-title: The multivariate skew‐normal distribution publication-title: Biometrika – volume: 70 start-page: 825 year: 2008 end-page: 848 article-title: Gaussian predictive process models for large spatial data sets publication-title: Journal of the Royal Statistical Society: Series B – volume: 68 start-page: 1305 year: 2019 end-page: 1326 article-title: Bayesian variable selection using spike‐and‐slab priors with application to high dimensional electroencephalography data by local modelling publication-title: Journal of the Royal Statistical Society: Series C – volume: 66 start-page: 783 year: 1971 end-page: 801 article-title: Elicitation of personal probabilities and expectations publication-title: Journal of the American Statistical Association – year: 2018 – volume: 102 start-page: 359 year: 2007 end-page: 378 article-title: Strictly proper scoring rules, prediction, and estimation publication-title: Journal of the American Statistical Association – volume: 18 start-page: 49 year: 1994 end-page: 65 article-title: Low resolution electromagnetic tomography: A new method for localizing electrical activity in the brain publication-title: International Journal of Psychophysiology – volume: 18 start-page: 5076 year: 2017 end-page: 5133 article-title: Bayesian inference for spatio‐temporal spike‐and‐slab priors publication-title: The Journal of Machine Learning Research – volume: 37 start-page: 1011 issue: 4 year: 2017 end-page: 1023 article-title: Estimating dynamic connectivity states in fMRI using regime‐switching factor models publication-title: IEEE Transactions on Medical Imaging – volume: 50 start-page: 297 year: 1988 end-page: 312 article-title: Estimation and model identification for continuous spatial processes publication-title: Journal of the Royal Statistical Society: Series B – volume: 144 start-page: 142 year: 2017 end-page: 152 article-title: Bayesian EEG source localization using a structured sparsity prior publication-title: NeuroImage – volume: 12 start-page: 55 year: 1970 end-page: 67 article-title: Ridge regression: Biased estimation for nonorthogonal problems publication-title: Technometrics – volume: 83 start-page: 1023 year: 1988 end-page: 1032 article-title: Bayesian variable selection in linear regression publication-title: Journal of the American Statistical Association – volume: 9 start-page: 449 year: 2014 end-page: 474 article-title: Bayesian regularization via graph laplacian publication-title: Bayesian Analysis – volume: 24 start-page: 2339 year: 2011 end-page: 2347 – volume: 61 start-page: 4658 year: 2013 end-page: 4672 article-title: Expectation‐maximization Gaussian‐mixture approximate message passing publication-title: IEEE Transactions on Signal Processing – volume: 44 start-page: 947 issue: 3 year: 2009 end-page: 966 article-title: A unified Bayesian framework for MEG/EEG source imaging publication-title: NeuroImage – volume: 39 start-page: 1104 issue: 3 year: 2008 end-page: 1120 article-title: Multiple sparse priors for the M/EEG inverse problem publication-title: NeuroImage – volume: 159 start-page: 29 year: 2006 end-page: 42 article-title: Source analysis of EEG oscillations using high‐resolution EEG and MEG publication-title: Progress in Brain Research – volume: 39 start-page: 318 year: 2008 end-page: 335 article-title: Bayesian M/EEG source reconstruction with spatio‐temporal priors publication-title: NeuroImage – start-page: 1745 year: 2014 end-page: 1753 – volume: 16 start-page: 678 issue: 3 year: 2002 end-page: 695 article-title: Anatomically informed basis functions for EEG source localization: Combining functional and anatomical constraints publication-title: NeuroImage – volume: 108 start-page: 540 year: 2013 end-page: 552 article-title: Tensor regression with applications in neuroimaging data analysis publication-title: Journal of the American Statistical Association – volume: 76 start-page: 463 year: 2014 end-page: 483 article-title: Regularized matrix regression publication-title: Journal of the Royal Statistical Society: Series B – volume: 13 start-page: 465 issue: 5 year: 2020 end-page: 481 article-title: Classification of high‐dimensional electroencephalography data with location selection using structured spike‐and‐slab prior publication-title: Statistical Analysis and Data Mining: The ASA Data Science Journal – volume: 31 start-page: 705 year: 2003 end-page: 767 article-title: Slice sampling publication-title: The Annals of Statistics – volume: 14 start-page: 1891 year: 2013 end-page: 1945 article-title: Generalized spike‐and‐slab priors for Bayesian group feature selection using expectation propagation publication-title: The Journal of Machine Learning Research – volume: 78 start-page: 505 year: 2016 end-page: 562 article-title: Of quantiles and expectiles: Consistent scoring functions, Choquet representations and forecast rankings publication-title: Journal of the Royal Statistical Society: Series B – volume: 58 start-page: 267 year: 1996 end-page: 288 article-title: Regression shrinkage and selection via the lasso publication-title: Journal of the Royal Statistical Society: Series B – volume: 10 start-page: 1286 year: 2016a end-page: 1316 article-title: Nonseparable dynamic nearest neighbor Gaussian process models for large spatio‐temporal data with an application to particulate matter analysis publication-title: The Annals of Applied Statistics – ident: e_1_2_8_40_1 doi: 10.1016/j.neuroimage.2008.02.059 – ident: e_1_2_8_42_1 doi: 10.1080/01621459.2013.776499 – ident: e_1_2_8_14_1 doi: 10.1186/1743-0003-5-25 – ident: e_1_2_8_31_1 doi: 10.1016/S0079-6123(06)59003-X – ident: e_1_2_8_23_1 doi: 10.1111/rssc.12369 – ident: e_1_2_8_17_1 doi: 10.1080/00401706.1970.10488634 – ident: e_1_2_8_41_1 doi: 10.1111/rssb.12031 – ident: e_1_2_8_13_1 doi: 10.1198/016214506000001437 – ident: e_1_2_8_19_1 doi: 10.1007/978-981-13-0908-3 – ident: e_1_2_8_27_1 doi: 10.1016/0167-8760(84)90014-X – start-page: 1745 volume-title: Advances in Neural Information Processing Systems year: 2014 ident: e_1_2_8_3_1 – ident: e_1_2_8_9_1 doi: 10.1080/01621459.2015.1044091 – ident: e_1_2_8_29_1 doi: 10.1080/01621459.1971.10482346 – ident: e_1_2_8_26_1 doi: 10.1111/biom.12126 – ident: e_1_2_8_12_1 doi: 10.1016/j.neuroimage.2007.09.048 – ident: e_1_2_8_18_1 doi: 10.1111/biom.12355 – ident: e_1_2_8_24_1 doi: 10.1002/sam.11477 – ident: e_1_2_8_38_1 doi: 10.1109/TSP.2013.2272287 – ident: e_1_2_8_25_1 doi: 10.1214/aos/1056562461 – ident: e_1_2_8_30_1 doi: 10.1037/0278-7393.6.2.174 – ident: e_1_2_8_21_1 doi: 10.1214/14-BA860 – ident: e_1_2_8_10_1 doi: 10.1016/j.jneumeth.2003.10.009 – ident: e_1_2_8_15_1 doi: 10.1093/biomet/57.1.97 – ident: e_1_2_8_22_1 doi: 10.1080/01621459.1988.10478694 – volume: 50 start-page: 297 year: 1988 ident: e_1_2_8_37_1 article-title: Estimation and model identification for continuous spatial processes publication-title: Journal of the Royal Statistical Society: Series B doi: 10.1111/j.2517-6161.1988.tb01729.x – volume: 14 start-page: 1891 year: 2013 ident: e_1_2_8_16_1 article-title: Generalized spike‐and‐slab priors for Bayesian group feature selection using expectation propagation publication-title: The Journal of Machine Learning Research – ident: e_1_2_8_28_1 doi: 10.1006/nimg.2002.1143 – ident: e_1_2_8_20_1 doi: 10.1016/j.bspc.2014.01.009 – ident: e_1_2_8_34_1 doi: 10.1109/TMI.2017.2780185 – ident: e_1_2_8_11_1 doi: 10.1111/rssb.12154 – ident: e_1_2_8_5_1 doi: 10.1093/biomet/83.4.715 – ident: e_1_2_8_8_1 doi: 10.1214/16-AOAS931 – ident: e_1_2_8_2_1 doi: 10.1080/01621459.1993.10476321 – volume: 18 start-page: 5076 year: 2017 ident: e_1_2_8_4_1 article-title: Bayesian inference for spatio‐temporal spike‐and‐slab priors publication-title: The Journal of Machine Learning Research – ident: e_1_2_8_36_1 doi: 10.1016/j.neuroimage.2007.07.062 – start-page: 2339 volume-title: Advances in Neural Information Processing Systems year: 2011 ident: e_1_2_8_35_1 – ident: e_1_2_8_7_1 doi: 10.1016/j.neuroimage.2016.08.064 – volume: 58 start-page: 267 year: 1996 ident: e_1_2_8_33_1 article-title: Regression shrinkage and selection via the lasso publication-title: Journal of the Royal Statistical Society: Series B doi: 10.1111/j.2517-6161.1996.tb02080.x – ident: e_1_2_8_39_1 doi: 10.1109/JSTSP.2016.2600023 – ident: e_1_2_8_32_1 doi: 10.1214/11-AOAS531 – ident: e_1_2_8_6_1 doi: 10.1111/j.1467-9868.2008.00663.x |
| SSID | ssj0035112 |
| Score | 2.238918 |
| Snippet | We present a scalable Bayesian modelling approach for identifying brain regions that respond to a certain stimulus and use them to classify subjects. More... |
| SourceID | proquest crossref wiley jstor |
| SourceType | Aggregation Database Index Database Publisher |
| StartPage | 107 |
| SubjectTerms | Alcoholism Algorithms Approximation Bayesian analysis Brain Case studies Classification Clustering Computation Correlation analysis Data Dimensional analysis EEG inverse problem Electroencephalography Estimation Feature selection Gibbs sampling Kronecker product likelihood approximation Localization Matrices Murphy diagram Performance evaluation Scores Simulation Statistical methods Stimuli Stimulus Time variable selection Variables |
| Title | Scalable spatio-temporal Bayesian analysis of high-dimensional electroencephalography data |
| URI | https://www.jstor.org/stable/48762893 https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fcjs.11592 https://www.proquest.com/docview/2509223237 |
| Volume | 49 |
| WOSCitedRecordID | wos000617161100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVWIB databaseName: Wiley Online Library - Journals customDbUrl: eissn: 1708-945X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0035112 issn: 0319-5724 databaseCode: DRFUL dateStart: 19970101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpZ1LS8QwEMcHUQ_rwcequL4I4sFL2TZJW4MnXV1E1kXcFfZkSZsEEdkVq4I3P4Kf0U_iJH3oHgTBWw9pKZnMzD_tzC8A-8IIpXgkPRb5zOOYEDzBQu35JsswgcexUs7SvbjfPxyNxNUMHFW9MAUfov7gZj3DxWvr4DLN29_Q0Ow-R38PBcbfOdtUhTuvudPr7k2vCsT2Fxmt-nTCmPIKLOTTdn3zVDoqKhKntOZPxepSTnfpXy-7DIul0iTHxdJYgRk9bsLCZY1pzZvQsFKzIDWvwu0AzWUbqUjuqqw_3z9KbtUDOZFv2nZbElkyTMjEEEs6xkHKng9QsD1IeaiOjRaPd7LCYRNbhroGw-7ZsHPulacveJmFCqL7UK7SQKEkiSjXDj0WZCaIMCJJneI2T4YZ6puIG98oySVNUQ0IExhFFZNsHWbHk7HeAGJ46PvKZyxlxhLHUs2NEdL4XGsWK9GCvcoGyWPB2EgKmjJNcOISN3EtWHfWqUdwG8dRbbVguzJXUvpfnqCwEyh8KItbcOAM8_uTk87FwF1s_n3oFjSoLW5xxWjbMPv89KJ3YD57RaM97ZYL8QuyGuKo |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpZ1LS8NAEMcHaQXrwUe1WJ-LePASmu5uEhe8-CpVaxFboSfDJruLiFRpVfDmR_Az-kmc3SRVD4LgLYdNCDs7M_9sZn4LsCOMUIqH0mOhzzyOCcETLNCeb9IUE3gUKeUs3Ym63b3BQFxOwX7RC5PxISYbbtYzXLy2Dm43pBtf1ND0bowOHwgMwGUesmivBOXjq9Z1p4jE9h8ZLRp1gojygizk08bk5h_5KCtJ_CE2v0tWl3Na8_972wWYy7UmOcgWxyJM6WEVZi8moNZxFSpWbGas5iW46aHBbCsVGbs664-395xcdU8O5au2_ZZE5hQT8mCIZR3jIGVPCMjoHiQ_VsfGi8dbWQCxiS1EXYZ-66R_1Pby8xe81GIF0YEoV0lToSgJKdcOPtZMTTPEmCR1gh96MkhR4YTc-EZJLmmCekCYplFUMclqUBo-DPUKEMMD31c-YwkzljmWaG6MkMbnWrNIiTpsF0aIHzPKRpzxlGmMExe7iatDzZlnMoLbSI56qw7rhb3i3APHMUo7gdKHsqgOu84yvz85PjrruYvVvw_dgpl2_6ITd06752tQobbUxZWmrUPpafSsN2A6fUEDjjbzVfkJQkLmmA |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpZ1LS8QwEMcH2RXRg-_F1VWDePBStk3S1oAX3XXxsS7iA_ZkSZsEEVkXVwVvfgQ_o5_ESdquehAEbz2koWQyM_-2M78AbAsjlOKR9FjkM49jQvAEC7XnmyzDBB7HSjlLd-Neb7ffF-cTsFf2wuR8iPEHN-sZLl5bB9dDZZpf1NDsboQOHwoMwFUeipBXoNq-6Fx3y0hs_5HRslEnjCkvyUI-bY5v_pGP8pLEH2Lzu2R1Oacz97-nnYfZQmuS_XxzLMCEHizCzNkY1DpahGkrNnNW8xLcXKLBbCsVGbk664-394JcdU8O5Ku2_ZZEFhQT8mCIZR3jIGVPCMjpHqQ4VsfGi-GtLIHYxBaiLsNV5_CqdeQV5y94mcUKogNRrtJAoSiJKNcOPhZkJogwJkmd4oueDDNUOBE3vlGSS5qiHhAmMIoqJlkNKoOHgV4BYnjo-8pnLGXGMsdSzY0R0vhcaxYrUYet0gjJMKdsJDlPmSa4cIlbuDrUnHnGI7iN5Ki36tAo7ZUUHjhKUNoJlD6UxXXYcZb5feakdXLpLlb_PnQTps7bnaR73Dtdg2lqK11cZVoDKk-Pz3odJrMXtN_jRrEpPwHz--YT |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Scalable+spatio%E2%80%90temporal+Bayesian+analysis+of+high%E2%80%90dimensional+electroencephalography+data&rft.jtitle=Canadian+journal+of+statistics&rft.au=Mohammed%2C+Shariq&rft.au=Dey%2C+Dipak+K.&rft.date=2021-03-01&rft.issn=0319-5724&rft.eissn=1708-945X&rft.volume=49&rft.issue=1&rft.spage=107&rft.epage=128&rft_id=info:doi/10.1002%2Fcjs.11592&rft.externalDBID=n%2Fa&rft.externalDocID=10_1002_cjs_11592 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0319-5724&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0319-5724&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0319-5724&client=summon |