Eelbrain, a Python toolkit for time-continuous analysis with temporal response functions

Even though human experience unfolds continuously in time, it is not strictly linear; instead, it entails cascading processes building hierarchical cognitive structures. For instance, during speech perception, humans transform a continuously varying acoustic signal into phonemes, words, and meaning,...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	eLife Ročník 12
Hlavní autori:	Brodbeck, Christian, Das, Proloy, Gillis, Marlies, Kulasingham, Joshua P, Bhattasali, Shohini, Gaston, Phoebe, Resnik, Philip, Simon, Jonathan Z
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England eLife Science Publications, Ltd 29.11.2023 eLife Sciences Publications Ltd eLife Sciences Publications, Ltd
Predmet:	Brain Brain - physiology Brain Mapping - methods Cognitive ability Computational neuroscience Electroencephalography Electroencephalography - methods Humans Hypotheses Information processing Neural coding Neuroscience open-source Physiological aspects Python (Programming language) reverse correlation Speech Speech - physiology Speech perception Speech Perception - physiology Statistical analysis STRF Time series Tools and Resources Variables United States STRF open-source human neuroscience reverse correlation Human
ISSN:	2050-084X, 2050-084X
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	Even though human experience unfolds continuously in time, it is not strictly linear; instead, it entails cascading processes building hierarchical cognitive structures. For instance, during speech perception, humans transform a continuously varying acoustic signal into phonemes, words, and meaning, and these levels all have distinct but interdependent temporal structures. Time-lagged regression using temporal response functions (TRFs ) has recently emerged as a promising tool for disentangling electrophysiological brain responses related to such complex models of perception. Here, we introduce the Eelbrain Python toolkit, which makes this kind of analysis easy and accessible. We demonstrate its use, using continuous speech as a sample paradigm, with a freely available EEG dataset of audiobook listening. A companion GitHub repository provides the complete source code for the analysis, from raw data to group-level statistics. More generally, we advocate a hypothesis-driven approach in which the experimenter specifies a hierarchy of time-continuous representations that are hypothesized to have contributed to brain responses, and uses those as predictor variables for the electrophysiological signal. This is analogous to a multiple regression problem, but with the addition of a time dimension. TRF analysis decomposes the brain signal into distinct responses associated with the different predictor variables by estimating a multivariate TRF (mTRF), quantifying the influence of each predictor on brain responses as a function of time(-lags). This allows asking two questions about the predictor variables: (1) Is there a significant neural representation corresponding to this predictor variable? And if so, (2) what are the temporal characteristics of the neural response associated with it? Thus, different predictor variables can be systematically combined and evaluated to jointly model neural processing at multiple hierarchical levels. We discuss applications of this approach, including the potential for linking algorithmic/representational theories at different cognitive levels to brain responses through computational models with appropriate linking hypotheses.
AbstractList	Even though human experience unfolds continuously in time, it is not strictly linear; instead, it entails cascading processes building hierarchical cognitive structures. For instance, during speech perception, humans transform a continuously varying acoustic signal into phonemes, words, and meaning, and these levels all have distinct but interdependent temporal structures. Time-lagged regression using temporal response functions (TRFs) has recently emerged as a promising tool for disentangling electrophysiological brain responses related to such complex models of perception. Here, we introduce the Eelbrain Python toolkit, which makes this kind of analysis easy and accessible. We demonstrate its use, using continuous speech as a sample paradigm, with a freely available EEG dataset of audiobook listening. A companion GitHub repository provides the complete source code for the analysis, from raw data to group-level statistics. More generally, we advocate a hypothesis-driven approach in which the experimenter specifies a hierarchy of time-continuous representations that are hypothesized to have contributed to brain responses, and uses those as predictor variables for the electrophysiological signal. This is analogous to a multiple regression problem, but with the addition of a time dimension. TRF analysis decomposes the brain signal into distinct responses associated with the different predictor variables by estimating a multivariate TRF (mTRF), quantifying the influence of each predictor on brain responses as a function of time(-lags). This allows asking two questions about the predictor variables: (1) Is there a significant neural representation corresponding to this predictor variable? And if so, (2) what are the temporal characteristics of the neural response associated with it? Thus, different predictor variables can be systematically combined and evaluated to jointly model neural processing at multiple hierarchical levels. We discuss applications of this approach, including the potential for linking algorithmic/representational theories at different cognitive levels to brain responses through computational models with appropriate linking hypotheses. Even though human experience unfolds continuously in time, it is not strictly linear; instead, it entails cascading processes building hierarchical cognitive structures. For instance, during speech perception, humans transform a continuously varying acoustic signal into phonemes, words, and meaning, and these levels all have distinct but interdependent temporal structures. Time-lagged regression using temporal response functions (TRFs) has recently emerged as a promising tool for disentangling electrophysiological brain responses related to such complex models of perception. Here, we introduce the Eelbrain Python toolkit, which makes this kind of analysis easy and accessible. We demonstrate its use, using continuous speech as a sample paradigm, with a freely available EEG dataset of audiobook listening. A companion GitHub repository provides the complete source code for the analysis, from raw data to group-level statistics. More generally, we advocate a hypothesis-driven approach in which the experimenter specifies a hierarchy of time-continuous representations that are hypothesized to have contributed to brain responses, and uses those as predictor variables for the electrophysiological signal. This is analogous to a multiple regression problem, but with the addition of a time dimension. TRF analysis decomposes the brain signal into distinct responses associated with the different predictor variables by estimating a multivariate TRF (mTRF), quantifying the influence of each predictor on brain responses as a function of time(-lags). This allows asking two questions about the predictor variables: (1) Is there a significant neural representation corresponding to this predictor variable? And if so, (2) what are the temporal characteristics of the neural response associated with it? Thus, different predictor variables can be systematically combined and evaluated to jointly model neural processing at multiple hierarchical levels. We discuss applications of this approach, including the potential for linking algorithmic/representational theories at different cognitive levels to brain responses through computational models with appropriate linking hypotheses.Even though human experience unfolds continuously in time, it is not strictly linear; instead, it entails cascading processes building hierarchical cognitive structures. For instance, during speech perception, humans transform a continuously varying acoustic signal into phonemes, words, and meaning, and these levels all have distinct but interdependent temporal structures. Time-lagged regression using temporal response functions (TRFs) has recently emerged as a promising tool for disentangling electrophysiological brain responses related to such complex models of perception. Here, we introduce the Eelbrain Python toolkit, which makes this kind of analysis easy and accessible. We demonstrate its use, using continuous speech as a sample paradigm, with a freely available EEG dataset of audiobook listening. A companion GitHub repository provides the complete source code for the analysis, from raw data to group-level statistics. More generally, we advocate a hypothesis-driven approach in which the experimenter specifies a hierarchy of time-continuous representations that are hypothesized to have contributed to brain responses, and uses those as predictor variables for the electrophysiological signal. This is analogous to a multiple regression problem, but with the addition of a time dimension. TRF analysis decomposes the brain signal into distinct responses associated with the different predictor variables by estimating a multivariate TRF (mTRF), quantifying the influence of each predictor on brain responses as a function of time(-lags). This allows asking two questions about the predictor variables: (1) Is there a significant neural representation corresponding to this predictor variable? And if so, (2) what are the temporal characteristics of the neural response associated with it? Thus, different predictor variables can be systematically combined and evaluated to jointly model neural processing at multiple hierarchical levels. We discuss applications of this approach, including the potential for linking algorithmic/representational theories at different cognitive levels to brain responses through computational models with appropriate linking hypotheses. Even though human experience unfolds continuously in time, it is not strictly linear; instead, it entails cascading processes building hierarchical cognitive structures. For instance, during speech perception, humans transform a continuously varying acoustic signal into phonemes, words, and meaning, and these levels all have distinct but interdependent temporal structures. Time-lagged regression using ) has recently emerged as a promising tool for disentangling electrophysiological brain responses related to such complex models of perception. Here, we introduce the Eelbrain Python toolkit, which makes this kind of analysis easy and accessible. We demonstrate its use, using continuous speech as a sample paradigm, with a freely available EEG dataset of audiobook listening. A companion GitHub repository provides the complete source code for the analysis, from raw data to group-level statistics. More generally, we advocate a hypothesis-driven approach in which the experimenter specifies a hierarchy of time-continuous representations that are hypothesized to have contributed to brain responses, and uses those as predictor variables for the electrophysiological signal. This is analogous to a multiple regression problem, but with the addition of a time dimension. TRF analysis decomposes the brain signal into distinct responses associated with the different predictor variables by estimating a multivariate TRF (mTRF), quantifying the influence of each predictor on brain responses as a function of time(-lags). This allows asking two questions about the predictor variables: (1) Is there a significant neural representation corresponding to this predictor variable? And if so, (2) what are the temporal characteristics of the neural response associated with it? Thus, different predictor variables can be systematically combined and evaluated to jointly model neural processing at multiple hierarchical levels. We discuss applications of this approach, including the potential for linking algorithmic/representational theories at different cognitive levels to brain responses through computational models with appropriate linking hypotheses. Even though human experience unfolds continuously in time, it is not strictly linear; instead, it entails cascading processes building hierarchical cognitive structures. For instance, during speech perception, humans transform a continuously varying acoustic signal into phonemes, words, and meaning, and these levels all have distinct but interdependent temporal structures. Time-lagged regression using temporal response functions (TRFs ) has recently emerged as a promising tool for disentangling electrophysiological brain responses related to such complex models of perception. Here, we introduce the Eelbrain Python toolkit, which makes this kind of analysis easy and accessible. We demonstrate its use, using continuous speech as a sample paradigm, with a freely available EEG dataset of audiobook listening. A companion GitHub repository provides the complete source code for the analysis, from raw data to group-level statistics. More generally, we advocate a hypothesis-driven approach in which the experimenter specifies a hierarchy of time-continuous representations that are hypothesized to have contributed to brain responses, and uses those as predictor variables for the electrophysiological signal. This is analogous to a multiple regression problem, but with the addition of a time dimension. TRF analysis decomposes the brain signal into distinct responses associated with the different predictor variables by estimating a multivariate TRF (mTRF), quantifying the influence of each predictor on brain responses as a function of time(-lags). This allows asking two questions about the predictor variables: (1) Is there a significant neural representation corresponding to this predictor variable? And if so, (2) what are the temporal characteristics of the neural response associated with it? Thus, different predictor variables can be systematically combined and evaluated to jointly model neural processing at multiple hierarchical levels. We discuss applications of this approach, including the potential for linking algorithmic/representational theories at different cognitive levels to brain responses through computational models with appropriate linking hypotheses.
Audience	Academic
Author	Gillis, Marlies Bhattasali, Shohini Kulasingham, Joshua P Resnik, Philip Das, Proloy Simon, Jonathan Z Brodbeck, Christian Gaston, Phoebe
Author_xml	– sequence: 1 givenname: Christian orcidid: 0000-0001-8380-639X surname: Brodbeck fullname: Brodbeck, Christian – sequence: 2 givenname: Proloy orcidid: 0000-0002-8807-042X surname: Das fullname: Das, Proloy – sequence: 3 givenname: Marlies orcidid: 0000-0002-3967-2950 surname: Gillis fullname: Gillis, Marlies – sequence: 4 givenname: Joshua P surname: Kulasingham fullname: Kulasingham, Joshua P – sequence: 5 givenname: Shohini orcidid: 0000-0002-6767-6529 surname: Bhattasali fullname: Bhattasali, Shohini – sequence: 6 givenname: Phoebe surname: Gaston fullname: Gaston, Phoebe – sequence: 7 givenname: Philip surname: Resnik fullname: Resnik, Philip – sequence: 8 givenname: Jonathan Z orcidid: 0000-0003-0858-0698 surname: Simon fullname: Simon, Jonathan Z
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/38018501$$D View this record in MEDLINE/PubMed https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-201030$$DView record from Swedish Publication Index (Linköpings universitet)
BookMark	eNptks9v0zAcxSM0xMbYiTuKxAUEKbbzw84JVWNApUogfmk3y3G-bl0Su9gOo_89TrvBMi05xHI-79l-fo-TI2MNJMlTjGa0LIs3sNQKZqxEmDxITggqUYZYcXl0a3ycnHm_QfGhBWO4fpQc5wzhUXOSXF5A1zihzetUpJ93YW1NGqztfuqQKuvSoHvIpDVBm8EOPhVGdDuvfXqlwzoN0G-tE13qwG-t8ZCqwcig4_BJ8lCJzsPZ9fc0-f7-4tv5x2z56cPifL7MZIXLkCmGSwIF5KWoJaWtZAqXUqg2J7RSKhfQyJrkBWpaRLGoWdvguiYQD1K2RYvz02Rx8G2t2PCt071wO26F5vsJ61ZcuKBlB5zIAhBGdQ6qKVBZCcQQYbIucENVK1H0yg5e_gq2QzNxe6d_zPdunR44iTb5yL898BHuoZVgQgxjIpv-MXrNV_Y3x4iynNHR4cW1g7O_BvCB99pL6DphIMbNCatLUsVdFhF9fgfd2MHF64hUTVBBGarof2ol4om1UTYuLEdTPqe0xrisGInU7B4qvi30Ot42KB3nJ4KXE8HYCPgTVmLwni--fpmyz26n8i-Om9JFAB8A6az3DhSXOoixNXEXuovp8LHbfN9tvu921Ly6o7mxvY_-CwA7-T0
CitedBy_id	crossref_primary_10_1371_journal_pone_0297826 crossref_primary_10_1523_ENEURO_0068_25_2025 crossref_primary_10_1016_j_isci_2024_110247 crossref_primary_10_1038_s41598_024_69602_1 crossref_primary_10_1523_JNEUROSCI_1143_24_2025 crossref_primary_10_1038_s41467_024_48126_2 crossref_primary_10_7554_eLife_97107_3 crossref_primary_10_1088_1741_2552_adfc9c crossref_primary_10_1523_JNEUROSCI_2294_24_2025 crossref_primary_10_1152_jn_00039_2025 crossref_primary_10_1111_nyas_70033 crossref_primary_10_1523_JNEUROSCI_0374_24_2024 crossref_primary_10_1162_nol_a_00169 crossref_primary_10_1016_j_dcn_2024_101470 crossref_primary_10_1523_ENEURO_0055_25_2025 crossref_primary_10_1162_nol_a_00155 crossref_primary_10_7554_eLife_97107 crossref_primary_10_1162_jocn_a_02214 crossref_primary_10_1162_jocn_a_02302 crossref_primary_10_1371_journal_pcbi_1013244 crossref_primary_10_1016_j_cognition_2025_106080
Cites_doi	10.1002/hbm.1058 10.1016/j.jneumeth.2007.03.024 10.1523/JNEUROSCI.0812-21.2021 10.1093/cercor/bht355 10.1214/07-EJS004 10.1523/JNEUROSCI.1396-16.2016 10.1073/pnas.1922033117 10.1162/jocn_a_01467 10.1016/B978-0-08-041847-6.50054-X 10.5281/zenodo.3509134 10.1016/j.neuroimage.2006.05.054 10.1109/TNSRE.2016.2571900 10.1371/journal.pbio.3000883 10.1016/j.cub.2018.01.080 10.1016/j.neuroimage.2008.03.061 10.3389/fnhum.2016.00604 10.1523/ENEURO.0441-17.2018 10.3389/fnins.2021.705621 10.1016/j.neuroimage.2013.10.027 10.1016/j.neuroimage.2020.117291 10.1080/23273798.2018.1499946 10.1111/j.1460-9568.2009.07055.x 10.1093/acprof:oso/9780195050387.001.0001 10.1046/j.1365-2656.2002.00618.x 10.1016/j.neuroimage.2020.116528 10.1073/pnas.1205381109 10.1152/jn.2001.85.6.2303 10.1016/j.neuroimage.2013.10.054 10.1088/1741-2552/aa66dd 10.1109/TBME.2022.3185005 10.1016/j.cub.2015.08.030 10.1080/23273798.2018.1546882 10.1080/09548980701609235 10.1016/j.neuron.2019.10.019 10.1016/j.cub.2019.04.067 10.7554/eLife.58077 10.1016/j.neuroimage.2018.01.042 10.1016/j.cub.2018.10.042 10.21105/joss.01026 10.1016/j.cophys.2020.07.014 10.1523/JNEUROSCI.2270-13.2013 10.3389/fnins.2018.00262 10.7554/eLife.72056 10.1016/j.neuroimage.2018.10.057 10.1080/net.12.3.289.316 10.1109/TSP.2010.2048103 10.3389/fnins.2021.665767 10.1016/s0010-0277(03)00139-2 10.21437/Interspeech.2017-1386 10.1016/j.neuroimage.2020.117586 10.1109/EMBC.2019.8857953 10.1038/s41586-020-2649-2 10.1016/j.neuroimage.2019.06.029 10.1371/journal.pone.0207741
ContentType	Journal Article
Copyright	2023, Brodbeck et al. COPYRIGHT 2023 eLife Science Publications, Ltd. 2023, Brodbeck et al. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2023, Brodbeck et al 2023 Brodbeck et al
Copyright_xml	– notice: 2023, Brodbeck et al. – notice: COPYRIGHT 2023 eLife Science Publications, Ltd. – notice: 2023, Brodbeck et al. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2023, Brodbeck et al 2023 Brodbeck et al
DBID	AAYXX CITATION CGR CUY CVF ECM EIF NPM ISR 3V. 7X7 7XB 88E 88I 8FE 8FH 8FI 8FJ 8FK ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2P M7P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI Q9U 7X8 5PM ABXSW ADTPV AOWAS D8T DG8 ZZAVC DOA
DOI	10.7554/eLife.85012
DatabaseName	CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Science ProQuest Central (Corporate) Health Medical collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials - QC Biological Science Collection ProQuest Central Natural Science Collection ProQuest One ProQuest Central Korea Proquest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Biological Science Collection ProQuest Health & Medical Collection Medical Database Science Database (ProQuest) Biological Science Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) SWEPUB Linköpings universitet full text SwePub SwePub Articles SWEPUB Freely available online SWEPUB Linköpings universitet SwePub Articles full text DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central ProQuest One Applied & Life Sciences ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic MEDLINE CrossRef Publicly Available Content Database
Database_xml	– sequence: 1 dbid: DOA name: Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Biology
EISSN	2050-084X
ExternalDocumentID	oai_doaj_org_article_2c4e01093efb4056a08028c941b7fdc0 oai_DiVA_org_liu_201030 PMC10783870 A779115682 38018501 10_7554_eLife_85012
Genre	Research Support, U.S. Gov't, Non-P.H.S Research Support, Non-U.S. Gov't Journal Article Research Support, N.I.H., Extramural
GeographicLocations	United States
GeographicLocations_xml	– name: United States
GrantInformation_xml	– fundername: NIDCD NIH HHS grantid: T32 DC017703 – fundername: NIH HHS grantid: R01 DC014085 – fundername: NIH HHS grantid: T32 DC017703 – fundername: NIH HHS grantid: R01 DC019394 – fundername: NIDCD NIH HHS grantid: R01 DC014085 – fundername: NIDCD NIH HHS grantid: R01 DC019394 – fundername: ; grantid: BCS 2043903 – fundername: ; grantid: R01 DC019394 – fundername: ; grantid: MURI N00014-18-1-2670 – fundername: ; grantid: SMA 1734892 – fundername: ; grantid: IIS 2207770 – fundername: ; grantid: R01 DC014085 – fundername: ; grantid: T32 DC017703 – fundername: ; grantid: SB 1SA0620N – fundername: ; grantid: BCS 1754284
GroupedDBID	53G 5VS 7X7 88E 88I 8FE 8FH 8FI 8FJ AAFWJ AAKDD AAYXX ABUWG ACGFO ACGOD ACPRK ADBBV ADRAZ AENEX AFFHD AFKRA AFPKN ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI CCPQU CITATION DIK DWQXO EMOBN FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HMCUK HYE IAO IEA IHR INH INR ISR ITC KQ8 LK8 M1P M2P M48 M7P M~E NQS OK1 PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO RHI RNS RPM UKHRP CGR CUY CVF ECM EIF NPM 3V. 7XB 8FK K9. PKEHL PQEST PQUKI Q9U 7X8 5PM ABXSW ADTPV AOWAS D8T DG8 H13 ZZAVC
ID	FETCH-LOGICAL-c615t-f8152e4e35a9c77dc8f15cafd3276ff3aebc92340bd071a98db1992e8815d4d13
IEDL.DBID	M2P
ISICitedReferencesCount	35
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001141821100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2050-084X
IngestDate	Fri Oct 03 12:53:41 EDT 2025 Tue Nov 04 16:24:30 EST 2025 Tue Nov 04 02:06:15 EST 2025 Sun Nov 09 13:11:40 EST 2025 Tue Oct 07 07:09:15 EDT 2025 Tue Nov 11 11:02:24 EST 2025 Tue Nov 04 18:22:20 EST 2025 Wed Nov 26 11:12:07 EST 2025 Sun Nov 16 02:01:16 EST 2025 Sat Nov 29 06:59:21 EST 2025 Tue Nov 18 21:50:44 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Keywords	STRF open-source human neuroscience reverse correlation Human
Language	English
License	2023, Brodbeck et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c615t-f8152e4e35a9c77dc8f15cafd3276ff3aebc92340bd071a98db1992e8815d4d13
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0001-8380-639X 0000-0002-6767-6529 0000-0003-0858-0698 0000-0002-8807-042X 0000-0002-3967-2950
OpenAccessLink	https://www.proquest.com/docview/2920478067?pq-origsite=%requestingapplication%
PMID	38018501
PQID	2920478067
PQPubID	2045579
ParticipantIDs	doaj_primary_oai_doaj_org_article_2c4e01093efb4056a08028c941b7fdc0 swepub_primary_oai_DiVA_org_liu_201030 pubmedcentral_primary_oai_pubmedcentral_nih_gov_10783870 proquest_miscellaneous_2895260284 proquest_journals_2920478067 gale_infotracmisc_A779115682 gale_infotracacademiconefile_A779115682 gale_incontextgauss_ISR_A779115682 pubmed_primary_38018501 crossref_citationtrail_10_7554_eLife_85012 crossref_primary_10_7554_eLife_85012
PublicationCentury	2000
PublicationDate	2023-11-29
PublicationDateYYYYMMDD	2023-11-29
PublicationDate_xml	– month: 11 year: 2023 text: 2023-11-29 day: 29
PublicationDecade	2020
PublicationPlace	England
PublicationPlace_xml	– name: England – name: Cambridge
PublicationTitle	eLife
PublicationTitleAlternate	Elife
PublicationYear	2023
Publisher	eLife Science Publications, Ltd eLife Sciences Publications Ltd eLife Sciences Publications, Ltd
Publisher_xml	– name: eLife Science Publications, Ltd – name: eLife Sciences Publications Ltd – name: eLife Sciences Publications, Ltd
References	Brodbeck (bib11) 2023 Di Liberto (bib20) 2015; 25 Brodbeck (bib10) 2022; 11 Nichols (bib46) 2002; 15 Crosse (bib13) 2016; 10 Gramfort (bib31) 2014; 86 Alday (bib1) 2019; 34 Crosse (bib15) 2021; 15 Broderick (bib12) 2018; 28 Babadi (bib2) 2010; 58 Weissbart (bib60) 2023 McAuliffe (bib44) 2017 Salverda (bib54) 2003; 90 David (bib19) 2013; 33 Brennan (bib5) 2019; 14 Brodbeck (bib6) 2018; 28 Presacco (bib50) 2019 Fiedler (bib26) 2019; 186 Ding (bib22) 2014; 88 Liberto (bib41) 2021; 227 David (bib18) 2007; 18 Rahman (bib51) 2020; 117 Gillis (bib30) 2021; 41 Maddox (bib42) 2018; 5 Lalor (bib38) 2006; 32 Patterson (bib49) 1992 Maris (bib43) 2007; 164 Etard (bib24) 2019; 200 Nunez (bib47) 2006 Donhauser (bib23) 2020; 105 Hastie (bib34) 2007; 1 Fiedler (bib25) 2017; 14 Ding (bib21) 2012; 109 Smith (bib55) 2009; 44 Harris (bib33) 2020; 585 Miran (bib45) 2018; 12 Biesmans (bib4) 2017; 25 Weissbart (bib59) 2020; 32 Bhattasali (bib3) 2020 Daube (bib17) 2019; 29 Kulasingham (bib36) 2020; 222 Fishbach (bib27) 2001; 85 Theunissen (bib57) 2001; 12 Kulasingham (bib37) 2023; 70 Lalor (bib39) 2010; 31 Brodbeck (bib7) 2018; 172 Brodbeck (bib8) 2020; 18 Vallat (bib58) 2018; 3 Fox (bib28) 2008 Reback (bib53) 2021 R Development Core Team (bib52) 2021 Freckleton (bib29) 2002; 71 Leahy (bib40) 2021; 15 O’Sullivan (bib48) 2015; 25 Sohoglu (bib56) 2020; 9 Heeris (bib35) 2018 Das (bib16) 2020; 211 Brodbeck (bib9) 2020; 18 Crosse (bib14) 2016; 36 Hamilton (bib32) 2020; 35
References_xml	– volume: 15 start-page: 1 year: 2002 ident: bib46 article-title: Nonparametric permutation tests for functional neuroimaging: A primer with examples publication-title: Human Brain Mapping doi: 10.1002/hbm.1058 – volume: 164 start-page: 177 year: 2007 ident: bib43 article-title: Nonparametric statistical testing of EEG- and MEG-data publication-title: Journal of Neuroscience Methods doi: 10.1016/j.jneumeth.2007.03.024 – volume: 41 start-page: 10316 year: 2021 ident: bib30 article-title: Neural markers of speech comprehension: measuring EEG tracking of linguistic speech representations, controlling the speech acoustics publication-title: The Journal of Neuroscience doi: 10.1523/JNEUROSCI.0812-21.2021 – volume: 25 start-page: 1697 year: 2015 ident: bib48 article-title: Attentional selection in a cocktail party environment can be decoded from single-trial EEG publication-title: Cerebral Cortex doi: 10.1093/cercor/bht355 – volume: 1 start-page: 1 year: 2007 ident: bib34 article-title: Forward stagewise regression and the monotone lasso publication-title: Electronic Journal of Statistics doi: 10.1214/07-EJS004 – volume-title: Applied Regression Analysis and Generalized Linear Models year: 2008 ident: bib28 – volume: 36 start-page: 9888 year: 2016 ident: bib14 article-title: Eye can hear clearly now: inverse effectiveness in natural audiovisual speech processing relies on long-term crossmodal temporal integration publication-title: The Journal of Neuroscience doi: 10.1523/JNEUROSCI.1396-16.2016 – volume: 117 start-page: 28442 year: 2020 ident: bib51 article-title: Simple transformations capture auditory input to cortex publication-title: PNAS doi: 10.1073/pnas.1922033117 – volume: 32 start-page: 155 year: 2020 ident: bib59 article-title: Cortical tracking of surprisal during continuous speech comprehension publication-title: Journal of Cognitive Neuroscience doi: 10.1162/jocn_a_01467 – volume-title: Complex Sounds and Auditory ImagesAuditory Physiology and Perception year: 1992 ident: bib49 doi: 10.1016/B978-0-08-041847-6.50054-X – volume-title: Zenodo year: 2021 ident: bib53 article-title: Pandas doi: 10.5281/zenodo.3509134 – volume: 32 start-page: 1549 year: 2006 ident: bib38 article-title: The VESPA: A method for the rapid estimation of A visual evoked potential publication-title: NeuroImage doi: 10.1016/j.neuroimage.2006.05.054 – volume: 25 start-page: 402 year: 2017 ident: bib4 article-title: Auditory-inspired speech envelope extraction methods for improved EEG-based auditory attention detection in a cocktail party scenario publication-title: IEEE Transactions on Neural Systems and Rehabilitation Engineering doi: 10.1109/TNSRE.2016.2571900 – volume: 18 year: 2020 ident: bib8 article-title: Neural speech restoration at the cocktail party: Auditory cortex recovers masked speech of both attended and ignored speakers publication-title: PLOS Biology doi: 10.1371/journal.pbio.3000883 – volume: 28 start-page: 803 year: 2018 ident: bib12 article-title: Electrophysiological correlates of semantic dissimilarity reflect the comprehension of natural, narrative speech publication-title: Current Biology doi: 10.1016/j.cub.2018.01.080 – volume: 44 start-page: 83 year: 2009 ident: bib55 article-title: Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference publication-title: NeuroImage doi: 10.1016/j.neuroimage.2008.03.061 – volume: 10 year: 2016 ident: bib13 article-title: The multivariate temporal response function (mTRF) Toolbox: A MATLAB toolbox for relating neural signals to continuous stimuli publication-title: Frontiers in Human Neuroscience doi: 10.3389/fnhum.2016.00604 – year: 2021 ident: bib52 article-title: R: A language and environment for statistical computing – start-page: 120 year: 2020 ident: bib3 article-title: The Alice Datasets – volume: 5 year: 2018 ident: bib42 article-title: Auditory brainstem responses to continuous natural speech in human listeners publication-title: eNeuro doi: 10.1523/ENEURO.0441-17.2018 – volume: 15 year: 2021 ident: bib15 article-title: Linear modeling of neurophysiological responses to speech and other continuous stimuli: methodological considerations for applied research publication-title: Frontiers in Neuroscience doi: 10.3389/fnins.2021.705621 – volume: 86 start-page: 446 year: 2014 ident: bib31 article-title: MNE software for processing MEG and EEG data publication-title: NeuroImage doi: 10.1016/j.neuroimage.2013.10.027 – volume: 222 year: 2020 ident: bib36 article-title: High gamma cortical processing of continuous speech in younger and older listeners publication-title: NeuroImage doi: 10.1016/j.neuroimage.2020.117291 – volume: 35 start-page: 573 year: 2020 ident: bib32 article-title: The revolution will not be controlled: natural stimuli in speech neuroscience publication-title: Language, Cognition and Neuroscience doi: 10.1080/23273798.2018.1499946 – volume: 31 start-page: 189 year: 2010 ident: bib39 article-title: Neural responses to uninterrupted natural speech can be extracted with precise temporal resolution publication-title: The European Journal of Neuroscience doi: 10.1111/j.1460-9568.2009.07055.x – volume-title: Electric Fields of the Brain: The Neurophysics of EEG year: 2006 ident: bib47 doi: 10.1093/acprof:oso/9780195050387.001.0001 – volume: 71 start-page: 542 year: 2002 ident: bib29 article-title: On the misuse of residuals in ecology: regression of residuals vs. multiple regression publication-title: Journal of Animal Ecology doi: 10.1046/j.1365-2656.2002.00618.x – volume: 211 year: 2020 ident: bib16 article-title: Neuro-current response functions: A unified approach to MEG source analysis under the continuous stimuli paradigm publication-title: NeuroImage doi: 10.1016/j.neuroimage.2020.116528 – volume: 109 start-page: 11854 year: 2012 ident: bib21 article-title: Emergence of neural encoding of auditory objects while listening to competing speakers publication-title: PNAS doi: 10.1073/pnas.1205381109 – volume: 85 start-page: 2303 year: 2001 ident: bib27 article-title: Auditory edge detection: A neural model for physiological and psychoacoustical responses to amplitude transients publication-title: Journal of Neurophysiology doi: 10.1152/jn.2001.85.6.2303 – volume: 88 start-page: 41 year: 2014 ident: bib22 article-title: Robust cortical entrainment to the speech envelope relies on the spectro-temporal fine structure publication-title: NeuroImage doi: 10.1016/j.neuroimage.2013.10.054 – volume: 14 year: 2017 ident: bib25 article-title: Single-channel in-ear-EEG detects the focus of auditory attention to concurrent tone streams and mixed speech publication-title: Journal of Neural Engineering doi: 10.1088/1741-2552/aa66dd – volume: 70 start-page: 88 year: 2023 ident: bib37 article-title: Algorithms for estimating time-locked neural response components in cortical processing of continuous speech publication-title: IEEE Transactions on Bio-Medical Engineering doi: 10.1109/TBME.2022.3185005 – volume: 25 start-page: 2457 year: 2015 ident: bib20 article-title: Low-frequency cortical entrainment to speech reflects phoneme-level processing publication-title: Current Biology doi: 10.1016/j.cub.2015.08.030 – volume: 34 start-page: 457 year: 2019 ident: bib1 article-title: M/EEG analysis of naturalistic stories: a review from speech to language processing publication-title: Language, Cognition and Neuroscience doi: 10.1080/23273798.2018.1546882 – volume-title: Software Heritage year: 2023 ident: bib11 article-title: Alice Dataset for Eelbrain – volume: 18 start-page: 191 year: 2007 ident: bib18 article-title: Estimating sparse spectro-temporal receptive fields with natural stimuli publication-title: Network doi: 10.1080/09548980701609235 – volume: 105 start-page: 385 year: 2020 ident: bib23 article-title: Two distinct neural timescales for predictive speech processing publication-title: Neuron doi: 10.1016/j.neuron.2019.10.019 – volume: 29 start-page: 1924 year: 2019 ident: bib17 article-title: Simple acoustic features can explain phoneme-based predictions of cortical responses to speech publication-title: Current Biology doi: 10.1016/j.cub.2019.04.067 – volume: 9 year: 2020 ident: bib56 article-title: Rapid computations of spectrotemporal prediction error support perception of degraded speech publication-title: eLife doi: 10.7554/eLife.58077 – volume: 172 start-page: 162 year: 2018 ident: bib7 article-title: Neural source dynamics of brain responses to continuous stimuli: Speech processing from acoustics to comprehension publication-title: NeuroImage doi: 10.1016/j.neuroimage.2018.01.042 – volume: 28 start-page: 3976 year: 2018 ident: bib6 article-title: Rapid transformation from auditory to linguistic representations of continuous speech publication-title: Current Biology doi: 10.1016/j.cub.2018.10.042 – volume: 3 year: 2018 ident: bib58 article-title: Pingouin: statistics in Python publication-title: Journal of Open Source Software doi: 10.21105/joss.01026 – volume: 18 start-page: 25 year: 2020 ident: bib9 article-title: Continuous speech processing publication-title: Current Opinion in Physiology doi: 10.1016/j.cophys.2020.07.014 – volume: 33 start-page: 19154 year: 2013 ident: bib19 article-title: Integration over multiple timescales in primary auditory cortex publication-title: The Journal of Neuroscience doi: 10.1523/JNEUROSCI.2270-13.2013 – volume-title: Filterbank Toolkit year: 2018 ident: bib35 article-title: Gammatone Filterbank Toolkit – volume-title: GitHub year: 2023 ident: bib60 article-title: pyEEG – volume: 12 year: 2018 ident: bib45 article-title: Real-time tracking of selective auditory attention from M/EEG: a bayesian filtering approach publication-title: Frontiers in Neuroscience doi: 10.3389/fnins.2018.00262 – volume: 11 year: 2022 ident: bib10 article-title: Parallel processing in speech perception with local and global representations of linguistic context publication-title: eLife doi: 10.7554/eLife.72056 – volume: 186 start-page: 33 year: 2019 ident: bib26 article-title: Late cortical tracking of ignored speech facilitates neural selectivity in acoustically challenging conditions publication-title: NeuroImage doi: 10.1016/j.neuroimage.2018.10.057 – volume: 12 start-page: 289 year: 2001 ident: bib57 article-title: Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli publication-title: Network doi: 10.1080/net.12.3.289.316 – volume: 58 start-page: 4013 year: 2010 ident: bib2 article-title: SPARLS: the sparse RLS algorithm publication-title: IEEE Transactions on Signal Processing doi: 10.1109/TSP.2010.2048103 – volume: 15 year: 2021 ident: bib40 article-title: An analytical framework of tonal and rhythmic hierarchy in natural music using the multivariate temporal response function publication-title: Frontiers in Neuroscience doi: 10.3389/fnins.2021.665767 – volume: 90 start-page: 51 year: 2003 ident: bib54 article-title: The role of prosodic boundaries in the resolution of lexical embedding in speech comprehension publication-title: Cognition doi: 10.1016/s0010-0277(03)00139-2 – start-page: 498 year: 2017 ident: bib44 article-title: Montreal forced aligner: trainable text-speech alignment using kaldi doi: 10.21437/Interspeech.2017-1386 – volume: 227 year: 2021 ident: bib41 article-title: Neural representation of linguistic feature hierarchy reflects second-language proficiency publication-title: NeuroImage doi: 10.1016/j.neuroimage.2020.117586 – start-page: 4148 year: 2019 ident: bib50 article-title: Real-time tracking of magnetoencephalographic neuromarkers during a dynamic attention-switching task doi: 10.1109/EMBC.2019.8857953 – volume: 585 start-page: 357 year: 2020 ident: bib33 article-title: Array programming with NumPy publication-title: Nature doi: 10.1038/s41586-020-2649-2 – volume: 200 start-page: 1 year: 2019 ident: bib24 article-title: Decoding of selective attention to continuous speech from the human auditory brainstem response publication-title: NeuroImage doi: 10.1016/j.neuroimage.2019.06.029 – volume: 14 year: 2019 ident: bib5 article-title: Hierarchical structure guides rapid linguistic predictions during naturalistic listening publication-title: PLOS ONE doi: 10.1371/journal.pone.0207741
SSID	ssj0000748819
Score	2.5280707
Snippet	Even though human experience unfolds continuously in time, it is not strictly linear; instead, it entails cascading processes building hierarchical cognitive...
SourceID	doaj swepub pubmedcentral proquest gale pubmed crossref
SourceType	Open Website Open Access Repository Aggregation Database Index Database Enrichment Source
SubjectTerms	Brain Brain - physiology Brain Mapping - methods Cognitive ability Computational neuroscience Electroencephalography Electroencephalography - methods Humans Hypotheses Information processing Neural coding Neuroscience open-source Physiological aspects Python (Programming language) reverse correlation Speech Speech - physiology Speech perception Speech Perception - physiology Statistical analysis STRF Time series Tools and Resources Variables
SummonAdditionalLinks	– databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3da9RAEF-kKPgifptaZZWiIMZmk0129_GqLQqlFD_KvS2b_dDQkJO7nND_3plNelyq4Iuv2QlJZiczv0lmfkPIPjcq97DRkJtYn_JSslSWgaeWl54VPBgT6RjOT8TpqZzP1dnWqC-sCRvogQfFHeSWe_x9U_hQA7ioDDaHSqs4q0VwNmbrmVBbyVT0wQIMk6mhIU9AyDzwJ03w72SZsXwSgiJT_5_-eCsgXS-WnFCKxjB0fJfcGfEjnQ33fY_c8N19cmuYKHn5gMyPfFvj1Ie31NCzSyQGoP1i0V40PQV4SnGUfIrl6U23hpyfmpGThOL3WDryVLV0OVTOeophL1rmQ_Lt-Ojr-4_pODwhtQBS-jRIiMye-6I0ygrhrAystCa4IhdVCIXxtQVwx7PaAcowSroaK1E9qK103LHiEdnpFp1_QmguK0jKvMP-LM68Mqxyual45oIpVF0n5M2VPrUdmcVxwEWrIcNA5euofB2Vn5D9jfDPgVDj72KHuDEbEWTBjgfANvRoG_pftpGQl7itGnkuOiyk-W7Wq5X-9OWzngkBbr6sJFzp9SgUFnDX1ox9CfDsSI01kdybSMKLaKfLV9ajR0ew0jgMjAsJmCAhLzbLeCYWt3UetlpDzltCWglAISGPB2PbPHcBCAIVkhA5McOJYqYrXfMj0oQz_EML7jghrwaLnZzzoTmfRWW2zVpjSUSR7f4PnT8lt3PAgdiumas9stMv1_4ZuWl_9c1q-Ty-pb8Bes9C4Q priority: 102 providerName: Directory of Open Access Journals
Title	Eelbrain, a Python toolkit for time-continuous analysis with temporal response functions
URI	https://www.ncbi.nlm.nih.gov/pubmed/38018501 https://www.proquest.com/docview/2920478067 https://www.proquest.com/docview/2895260284 https://pubmed.ncbi.nlm.nih.gov/PMC10783870 https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-201030 https://doaj.org/article/2c4e01093efb4056a08028c941b7fdc0
Volume	12
WOSCitedRecordID	wos001141821100001&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: PRVAON databaseName: Directory of Open Access Journals customDbUrl: eissn: 2050-084X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000748819 issn: 2050-084X databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2050-084X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000748819 issn: 2050-084X databaseCode: M~E dateStart: 20120101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 2050-084X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000748819 issn: 2050-084X databaseCode: M7P dateStart: 20120101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health Medical collection customDbUrl: eissn: 2050-084X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000748819 issn: 2050-084X databaseCode: 7X7 dateStart: 20120101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2050-084X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000748819 issn: 2050-084X databaseCode: BENPR dateStart: 20120101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2050-084X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000748819 issn: 2050-084X databaseCode: PIMPY dateStart: 20120101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database (ProQuest) customDbUrl: eissn: 2050-084X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000748819 issn: 2050-084X databaseCode: M2P dateStart: 20120101 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3db9MwELfYChIvfH8ERmXQBBIirEmc2HlCHWxi0lZVA6byZDmOPSKqZDQt0v577hy3EEC88OKH-KLEvvN92OffEbLLVB4bYDTEJtqELBVRKFLLQs1SEyXMKuXgGM6O-WQiZrN86jfcWp9WudaJTlGXjcY98j2sqsS4AOX65uJbiFWj8HTVl9DYIgPwbCJM6TqJp5s9FjCPAixedy2Pg-HcM8eVNa9FOoriniFyeP1_auVfzNLvKZM9YFFnjA5v_u8wbpEb3g2l405ubpMrpr5DrnWFKS_vktmBmRdYPOIVVXR6ifgCdNk086_VkoKXS7EifYhZ7lW9alYtVR7ahOK2LvVwV3O66BJwDUXr6QT8Hvl0ePDx7fvQ12AINfg6y9AKMPCGmSRVuea81MJGqVa2TGKeWZsoU2jwEdmoKMFZUbkoC0xoNTDvacnKKLlPtuumNg8JjUUGsZ0p8ZoXi0yuoqyMVcZGpVVJXhQBeblmiNQeoBzrZMwlBCrIPem4Jx33ArK7Ib7ocDn-TraPnN2QIJi2e9AszqVfmzLWzOAJYWJsAf5rpvD-sdA5iwpuSz0KyDOUC4lwGTXm45yrVdvKow-ncsw5WIs0E_ClF57INvDXWvnrDTB2RNjqUe70KGE96373WnKk1yet_Ck2AXm66cY3MUeuNsBqCaFzCtEp-BsBedBJ62bcCTgiOCEBET057k1Mv6euvji08QgPekGrB-R5J_K9d95VZ2M3mfNqJTGzIhk9-vcAHpPrMTiKeJ8zznfI9nKxMk_IVf19WbWLIdniM-5aMSSD_YPJ9HTo9kmGbmljy6EdTI9Opp9_AN79WGQ
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3db9MwELfGAMEL3x-BAQYNkBBhjeMkzgNChW1atVJVY0x9M45jj4iSjCYF9Z_ib-QuSQsBxNseeK3PbW3fp333O0I2uYqZgYOG2EQblwfCc0Vguat5YDyfW6VqOIajYTQaickkHq-R78taGEyrXOrEWlGnhcY78i3sqsQjAcr11ckXF7tG4evqsoVGwxb7ZvENQrby5WAbzvcxY7s7h2_23LargKvBeleuFWCyDDd-oGIdRakW1gu0sqnPotBaX5lEg9fDe0kK5lfFIk0wRdMImJfy1PPhe8-QsxyRxTBVkI1XdzpgjoEqbsoAIzDUW2aYWfNCBD2PdQxf3R_gTyvwixn8PUWzA2RaG7_dy__btl0hl1o3m_YbubhK1kx-jZxvGm8urpPJjpkm2BzjOVV0vED8BFoVxfRTVlHw4mmVfTYuZvFn-byYl1S10C0Ur61pC-c1pbMmwdhQ9A5qAb5B3p_Ksm6S9bzIzW1CmQghdjUplrFxz8TKC1OmQt5LrfLjJHHIsyUDSN0CsGMfkKmEQAy5RdbcImtuccjmivikwR35O9lr5KQVCYKF1x8Us2PZ6h7JNDf4Auobm4B_HiqsrxY65l4S2VT3HPII-VAiHEiO-UbHal6WcvDuQPajCKxhEAr4pactkS3gX2vVlm_A2hFBrEO50aEEfaW7w0tOla2-LOVPNnXIw9UwzsQcwNzAUUsm4gCib_CnHHKrkY7Vun1wtHBDHCI6ctPZmO5Inn2s0dQ9fMgGq-WQJ42IdeZsZ0f9ejOn2Vxi5ojfu_PvBTwgF_YO3w7lcDDav0suMnCKsXaVxRtkvZrNzT1yTn-tsnJ2v1YelHw4bbH7AWVbrz0
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lj9MwELaWLiAuvB-BBQxaQEKENomTOAeECt2KaktVLbAqJ-M49hJRkqVJQf1r_DpmkrQQQNz2wLUet7U9T3vmG0J2mYxcDQcNsYnSNvO5Y3PfMFsxXzseM1JWcAyH43Ay4bNZNN0i39e1MJhWudaJlaJOcoV35F3sqsRCDsq1a5q0iOlg-Pz4i40dpPCldd1Oo2aRfb36BuFb8Ww0gLN-4LrDvbcvX9lNhwFbgSUvbcPBfGmmPV9GKgwTxY3jK2kSzw0DYzypYwUeEOvFCZhiGfEkxnRNzWFewhLHg-89RbbBJWduh2xPR6-n7zc3PGCcgS6qiwJDMNtdPU6Nfsr9nuO2zGDVLeBPm_CLUfw9YbMFa1qZwuGF_3kTL5LzjQNO-7XEXCJbOrtMztQtOVdXyGxPz2Nsm_GESjpdIbICLfN8_iktKfj3tEw_axvz-9NsmS8LKhtQF4oX2rQB-prTRZ16rCn6DZVoXyXvTmRZ10gnyzN9g1CXBxDV6gQL3JijI-kEiSsD1kuM9KI4tsjjNTMI1UCzY4eQuYAQDTlHVJwjKs6xyO6G-LhGJPk72Qvkqg0JwohXH-SLI9FoJeEqpvFt1NMmBs89kFh5zVXEnDg0iepZ5D7ypECgkAy55Ugui0KM3hyIfhiCnfQDDr_0qCEyOfxrJZvCDlg7You1KHdalKDJVHt4zbWi0aSF-MmyFrm3GcaZmB2YaThq4fLIh7gcPC2LXK8lZbNuD1ww3BCL8JYMtTamPZKlHyucdQefuMGeWeRhLW6tOYP0sF9t5jxdCswp8Xo3_72Au-QsSJsYjyb7t8g5F7xlLGp1ox3SKRdLfZucVl_LtFjcaTQJJR9OWu5-AEE-uYY
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=Eelbrain%2C+a+Python+toolkit+for+time-continuous+analysis+with+temporal+response+functions&rft.jtitle=eLife&rft.au=Brodbeck%2C+Christian&rft.au=Das%2C+Proloy&rft.au=Gillis%2C+Marlies&rft.au=Kulasingham%2C+Joshua+P&rft.date=2023-11-29&rft.issn=2050-084X&rft.eissn=2050-084X&rft.volume=12&rft_id=info:doi/10.7554%2FeLife.85012&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2050-084X&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2050-084X&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2050-084X&client=summon