Exploring the temporal dynamics of speech production with EEG and group ICA

Speech production is a complex skill whose neural implementation relies on a large number of different regions in the brain. How neural activity in these different regions varies as a function of time during the production of speech remains poorly understood. Previous MEG studies on this topic have...

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Autores:
Janssen, Niels
van der Meij, Maartje
López-Pérez, Pedro Javier
Barber, Horacio A.
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/6224
Acceso en línea:
https://hdl.handle.net/11323/6224
https://doi.org/10.1038/s41598-020-60301-1
https://repositorio.cuc.edu.co/
Palabra clave:
Neural implementation
Neural activity
MEG
Technique EEG
Rights
openAccess
License
CC0 1.0 Universal
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oai_identifier_str oai:repositorio.cuc.edu.co:11323/6224
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Exploring the temporal dynamics of speech production with EEG and group ICA
title Exploring the temporal dynamics of speech production with EEG and group ICA
spellingShingle Exploring the temporal dynamics of speech production with EEG and group ICA
Neural implementation
Neural activity
MEG
Technique EEG
title_short Exploring the temporal dynamics of speech production with EEG and group ICA
title_full Exploring the temporal dynamics of speech production with EEG and group ICA
title_fullStr Exploring the temporal dynamics of speech production with EEG and group ICA
title_full_unstemmed Exploring the temporal dynamics of speech production with EEG and group ICA
title_sort Exploring the temporal dynamics of speech production with EEG and group ICA
dc.creator.fl_str_mv Janssen, Niels
van der Meij, Maartje
López-Pérez, Pedro Javier
Barber, Horacio A.
dc.contributor.author.spa.fl_str_mv Janssen, Niels
van der Meij, Maartje
López-Pérez, Pedro Javier
Barber, Horacio A.
dc.subject.spa.fl_str_mv Neural implementation
Neural activity
MEG
Technique EEG
topic Neural implementation
Neural activity
MEG
Technique EEG
description Speech production is a complex skill whose neural implementation relies on a large number of different regions in the brain. How neural activity in these different regions varies as a function of time during the production of speech remains poorly understood. Previous MEG studies on this topic have concluded that activity proceeds from posterior to anterior regions of the brain in a sequential manner. Here we tested this claim using the EEG technique. Specifically, participants performed a picture naming task while their naming latencies and scalp potentials were recorded. We performed group temporal Independent Component Analysis (group tICA) to obtain temporally independent component timecourses and their corresponding topographic maps. We identified fifteen components whose estimated neural sources were located in various areas of the brain. The trial-by-trial component timecourses were predictive of the naming latency, implying their involvement in the task. Crucially, we computed the degree of concurrent activity of each component timecourse to test whether activity was sequential or parallel. Our results revealed that these fifteen distinct neural sources exhibit largely concurrent activity during speech production. These results suggest that speech production relies on neural activity that takes place in parallel networks of distributed neural sources.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-04-20T16:23:06Z
dc.date.available.none.fl_str_mv 2020-04-20T16:23:06Z
dc.date.issued.none.fl_str_mv 2020
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
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dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
format http://purl.org/coar/resource_type/c_6501
status_str acceptedVersion
dc.identifier.issn.spa.fl_str_mv 2045-2322
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/6224
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1038/s41598-020-60301-1
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 2045-2322
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/6224
https://doi.org/10.1038/s41598-020-60301-1
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. Bressler, S. L. Large-scale cortical networks and cognition. Brain Research Reviews 20(3), 288–304 (1995).
2. Chartier, J., Anumanchipalli, G. K., Johnson, K. & Chang, E. F. Encoding of articulatory kinematic trajectories in human speech sensorimotor cortex. Neuron 98((5) jun), 1042–1054.e4 (2018).
3. Fried, I., Ojemann, G. A. & Fetz, E. E. Language-related potentials specifc to human language cortex. Science 212(4492), 353–356 (1981).
4. Indefrey, P. & Levelt, W. J. Te spatial and temporal signatures of word production components. Cognition 92(1), 101–144 (2004).
5. Jürgens, U. Neural pathways underlying vocal control. Neuroscience & Biobehavioral Reviews 26(2), 235–258 (2002).
6. Piai, V. et al. Direct brain recordings reveal hippocampal rhythm underpinnings of language processing. Proceedings of the National Academy of Sciences 113(40), 11366–11371 (2016).
7. Simonyan, K. & Horwitz, B. Laryngeal motor cortex and control of speech in humans. Te Neuroscientist 17(feb 2), 197–208 (2011).
8. Etard, O. et al. Picture naming without brocaas and wernickeas area. Neuroreport 11(3), 617–622 (2000).
9. Geranmayeh, F. et al. Te contribution of the inferior parietal cortex to spoken language production. Brain and language 121(1), 47–57 (2012).
10. Janssen, N. & Mendieta, C. C. R. Te dynamics of speech motor control revealed with time-resolved fmri. Cerebral Cortex, (2019).
11. Murtha, S., Chertkow, H., Beauregard, M. & Evans, A. Te neural substrate of picture naming. Journal of cognitive neuroscience 11(4), 399–423 (1999).
12. Price, C. J. A review and synthesis of the frst 20 years of pet and fmri studies of heard speech, spoken language and reading. Neuroimage 62(2), 816–847 (2012).
13. Hulten, A., Vihla, M., Laine, M. & Salmelin, R. Accessing newly learned names and meanings in the native language. Human brain mapping 30(3), 976–989 (2009).
14. Liljeström, M., Hulten, A., Parkkonen, L. & Salmelin, R. Comparing meg and fmri views to naming actions and objects. Human brain mapping 30(6), 1845–1856 (2009).
15. Maess, B., Friederici, A. D., Damian, M., Meyer, A. S. & Levelt, W. J. Semantic category interference in overt picture naming: Sharpening current density localization by pca. Journal of cognitive neuroscience 14(3), 455–462 (2002).
16. Salmelin, R., Hari, R., Lounasmaa, O. & Sams, M. Dynamics of brain activation during picture naming. Nature 368(6470), 463–465 (1994).
17. Sörös, P., Cornelissen, K., Laine, M. & Salmelin, R. Naming actions and objects: cortical dynamics in healthy adults and in an anomic patient with a dissociation in action/object naming. Neuroimage 19(4), 1787–1801 (2003).
18. Vihla, M., Laine, M. & Salmelin, R. Cortical dynamics of visual/semantic vs. phonological analysis in picture confrontation. Neuroimage 33(2), 732–738 (2006).
19. Indefrey, P. On putative shortcomings and dangerous future avenues: response to strijkers & costa. Language, Cognition and Neuroscience 31(4), 517–520 (2016).
20. Fargier, R. & Laganaro, M. Spatio-temporal dynamics of referential and inferential naming: Diferent brain and cognitive operations to lexical selection. Brain Topography 30(jun 2), 182–197 (2016).
21. Kober, H. et al. New approach to localize speech relevant brain areas and hemispheric dominance using spatially filtered magnetoencephalography. Human brain mapping 14(4), 236–250 (2001).
22. Miozzo, M., Pulvermüller, F. & Hauk, O. Early parallel activation of semantics and phonology in picture naming: Evidence from a multiple linear regression meg study. Cerebral Cortex 25(10), 3343–3355 (2014).
23. Munding, D., Dubarry, A.-S. & Alario, F.-X. On the cortical dynamics of word production: A review of the meg evidence. Language, Cognition and Neuroscience 31(4), 441–462 (2016).
24. Rahman, R. A. & Sommer, W. Does phonological encoding in speech production always follow the retrieval of semantic knowledge?: Electrophysiological evidence for parallel processing. Cognitive Brain Research 16(3), 372–382 (2003).
25. Strijkers, K. & Costa, A. Te cortical dynamics of speaking: Present shortcomings and future avenues. Language, Cognition and Neuroscience 31(4), 484–503 (2016).
26. Hämäläinen, M., Hari, R., Ilmoniemi, R. J., Knuutila, J. & Lounasmaa, O. V. Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain. Reviews of modern Physics 65(2), 413 (1993).
27. Delorme, A., Palmer, J., Onton, J., Oostenveld, R. & Makeig, S. Independent eeg sources are dipolar. PloS one 7(2), e30135 (2012).
28. DiRusso, F., Martínez, A., Sereno, M. I., Pitzalis, S. & Hillyard, S. A. Cortical sources of the early components of the visual evoked potential. Human brain mapping 15(2), 95–111 (2002).
29. Riès, S., Janssen, N., Burle, B. & Alario, F.-X. Response-locked brain dynamics of word production. PLoS One 8(3), e58197 (2013).
30. Glaser, W. R. Picture naming. Cognition 42(1), 61–105 (1992).
31. Handy, T. C. Event-related potentials: A methods handbook. MIT press, (2005).
32. Costa, A., Strijkers, K., Martin, C. & Tierry, G. Te time course of word retrieval revealed by event-related brain potentials during overt speech. Proceedings of the National Academy of Sciences 106(50), 21442–21446 (2009).
33. Szekely, A. et al. A new on-line resource for psycholinguistic studies. Journal of memory and language 51(2), 247–250 (2004).
34. Delorme, A. & Makeig, S. Eeglab: an open source toolbox for analysis of single-trial eeg dynamics including independent component analysis. Journal of neuroscience methods 134(1), 9–21 (2004).
35. Winkler, I., Debener, S., Müller, K.-R. & Tangermann, M. On the infuence of high-pass fltering on ica-based artifact reduction in eeg-erp. In Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, pages 4101–4105. IEEE, (2015).
36. Acunzo, D. J., MacKenzie, G. & van Rossum, M. C. Systematic biases in early erp and erf components as a result of high-pass fltering. Journal of neuroscience methods 209(1), 212–218 (2012).
37. Maess, B., Schröger, E. & Widmann, A. High-pass filters and baseline correction in m/eeg analysis. commentary on:how inappropriate high-pass flters can produce artefacts and incorrect conclusions in erp studies of language and cognition. Journal of neuroscience methods 266, 164–165 (2016).
38. Tanner, D., Morgan-Short, K. & Luck, S. J. How inappropriate high-pass filters can produce artifactual effects and incorrect conclusions in erp studies of language and cognition. Psychophysiology 52(8), 997–1009 (2015).
39. Widmann, A., Schröger, E. & Maess, B. Digital flter design for electrophysiological data-a practical approach. Journal of neuroscience methods 250, 34–46 (2015).
40. Porcaro, C., Medaglia, M. T. & Krott, A. Removing speech artifacts from electroencephalographic recordings during overt picture naming. NeuroImage 105, 171–180 (2015).
41. Comon, P. Independent component analysis, a new concept? Signal processing 36(3), 287–314 (1994).
42. Petersen, K., Hansen, L. K., Kolenda, T., Rostrup, E. & Strother, S. On the independent components of functional neuroimages. In Tird international conference on independent component analysis and blind source separation, pages 615–620 (2000).
43. Makeig, S. et al. Dynamic brain sources of visual evoked responses. Science 295(5555), 690–694 (2002).
44. Hyvarinen, A. Fast and robust fxed-point algorithms for independent component analysis. IEEE transactions on Neural Networks 10(3), 626–634 (1999).
45. Kovacevic, N. & McIntosh, A. R. Groupwise independent component decomposition of eeg data and partial least square analysis. Neuroimage 35(3), 1103–1112 (2007).
46. Makeig, S. et al. Electroencephalographic brain dynamics following manually responded visual targets. PLoS biology 2(6), e176 (2004).
47. Artoni, F., Delorme, A. & Makeig, S. Applying dimension reduction to EEG data by principal component analysis reduces the quality of its subsequent independent component decomposition. NeuroImage 175(jul), 176–187 (2018).
48. Makeig, S., Debener, S., Onton, J. & Delorme, A. Mining event-related brain dynamics. Trends in cognitive sciences 8(5), 204–210 (2004).
49. Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on mri scans into gyral based regions of interest. Neuroimage 31(3), 968–980 (2006).
50. Delorme, A., Sejnowski, T. & Makeig, S. Enhanced detection of artifacts in eeg data using higher-order statistics and independent component analysis. Neuroimage 34(4), 1443–1449 (2007).
51. Joyce, C. A., Gorodnitsky, I. F. & Kutas, M. Automatic removal of eye movement and blink artifacts from eeg data using blind component separation. Psychophysiology 41(2), 313–325 (2004).
52. Weber, M. J. & Tompson-Schill, S. L. Functional neuroimaging can support causal claims about brain function. Journal of cognitive neuroscience 22(11), 2415–2416 (2010).
53. Janssen, N., Hernández-Cabrera, J. A., van der Meij, M. & Barber, H. A. Tracking the time course of competition during word production: Evidence for a post-retrieval mechanism of confict resolution. Cerebral Cortex 25(9), 2960–2969 (2014).
54. Guthrie, D. & Buchwald, J. S. Signifcance testing of diference potentials. Psychophysiology 28(2), 240–244 (1991).
55. Protopapas, A. Check vocal: A program to facilitate checking the accuracy and response time of vocal responses from dmdx. Behavior Research Methods 39(4), 859–862 (2007).
56. Delorme, A., Miyakoshi, M., Jung, T.-P. & Makeig, S. Grand average erp-image plotting and statistics: A method for comparing variability in event-related single-trial eeg activities across subjects and conditions. Journal of neuroscience methods 250, 3–6 (2015).
57. Geranmayeh, F., Wise, R. J. S., Mehta, A. & Leech, R. Overlapping networks engaged during spoken language production and its cognitive control. Journal of Neuroscience 34(jun 26), 8728–8740 (2014).
58. Riecker, A. et al. fmri reveals two distinct cerebral networks subserving speech motor control. Neurology 64(4), 700–706 (2005).
59. Hassan, M. et al. Dynamic reorganization of functional brain networks during picture naming. Cortex 73, 276–288 (2015).
60. Song, J. et al. Eeg source localization: sensor density and head surface coverage. Journal of neuroscience methods 256, 9–21 (2015).
61. Cisek, P. & Kalaska, J. F. Neural mechanisms for interacting with a world full of action choices. Annual review of neuroscience 33, 269–298 (2010).
62. Ledberg, A., Bressler, S. L., Ding, M., Coppola, R. & Nakamura, R. Large-scale visuomotor integration in the cerebral cortex. Cerebral cortex 17(1), 44–62 (2006).
63. Liang, H., Bressler, S. L., Ding, M., Truccolo, W. A. & Nakamura, R. Synchronized activity in prefrontal cortex during anticipation of visuomotor processing. Neuroreport 13(16), 2011–2015 (2002).
64. Fetz, E. E. Are movement parameters recognizably coded in the activity of single neurons? Behavioral and Brain Sciences 15, 679–690 (1992).
65. Smith, S. M. et al. Functional connectomics from resting-state fmri. Trends in cognitive sciences 17(12), 666–682 (2013).
66. Fries, P. A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends in Cognitive Sciences 9(oct 10), 474–480 (2005).
67. Kornhuber, H. H. & Deecke, L. Brain potential changes in voluntary and passive movements in humans: readiness potential and reaferent potentials. Pfügers Archiv-European Journal of Physiology 468(7), 1115–1124 (2016).
68. Nickerson, L. D., Smith, S. M., Öngür, D. & Beckmann, C. F. Using dual regression to investigate network shape and amplitude in functional connectivity analyses. Frontiers in Neuroscience (11, mar 2017).
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spelling Janssen, Nielsvan der Meij, MaartjeLópez-Pérez, Pedro JavierBarber, Horacio A.2020-04-20T16:23:06Z2020-04-20T16:23:06Z20202045-2322https://hdl.handle.net/11323/6224https://doi.org/10.1038/s41598-020-60301-1Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Speech production is a complex skill whose neural implementation relies on a large number of different regions in the brain. How neural activity in these different regions varies as a function of time during the production of speech remains poorly understood. Previous MEG studies on this topic have concluded that activity proceeds from posterior to anterior regions of the brain in a sequential manner. Here we tested this claim using the EEG technique. Specifically, participants performed a picture naming task while their naming latencies and scalp potentials were recorded. We performed group temporal Independent Component Analysis (group tICA) to obtain temporally independent component timecourses and their corresponding topographic maps. We identified fifteen components whose estimated neural sources were located in various areas of the brain. The trial-by-trial component timecourses were predictive of the naming latency, implying their involvement in the task. Crucially, we computed the degree of concurrent activity of each component timecourse to test whether activity was sequential or parallel. Our results revealed that these fifteen distinct neural sources exhibit largely concurrent activity during speech production. These results suggest that speech production relies on neural activity that takes place in parallel networks of distributed neural sources.Janssen, Nielsvan der Meij, Maartje-will be generated-orcid-0000-0002-9469-9582-600López-Pérez, Pedro Javier-will be generated-orcid-0000-0003-1605-9544-600Barber, Horacio A.engScientific ReportsCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Neural implementationNeural activityMEGTechnique EEGExploring the temporal dynamics of speech production with EEG and group ICAArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Bressler, S. L. Large-scale cortical networks and cognition. Brain Research Reviews 20(3), 288–304 (1995).2. Chartier, J., Anumanchipalli, G. K., Johnson, K. & Chang, E. F. Encoding of articulatory kinematic trajectories in human speech sensorimotor cortex. Neuron 98((5) jun), 1042–1054.e4 (2018).3. Fried, I., Ojemann, G. A. & Fetz, E. E. Language-related potentials specifc to human language cortex. Science 212(4492), 353–356 (1981).4. Indefrey, P. & Levelt, W. J. Te spatial and temporal signatures of word production components. Cognition 92(1), 101–144 (2004).5. Jürgens, U. Neural pathways underlying vocal control. Neuroscience & Biobehavioral Reviews 26(2), 235–258 (2002).6. Piai, V. et al. Direct brain recordings reveal hippocampal rhythm underpinnings of language processing. Proceedings of the National Academy of Sciences 113(40), 11366–11371 (2016).7. Simonyan, K. & Horwitz, B. Laryngeal motor cortex and control of speech in humans. Te Neuroscientist 17(feb 2), 197–208 (2011).8. Etard, O. et al. Picture naming without brocaas and wernickeas area. Neuroreport 11(3), 617–622 (2000).9. Geranmayeh, F. et al. Te contribution of the inferior parietal cortex to spoken language production. Brain and language 121(1), 47–57 (2012).10. Janssen, N. & Mendieta, C. C. R. Te dynamics of speech motor control revealed with time-resolved fmri. Cerebral Cortex, (2019).11. Murtha, S., Chertkow, H., Beauregard, M. & Evans, A. Te neural substrate of picture naming. Journal of cognitive neuroscience 11(4), 399–423 (1999).12. Price, C. J. A review and synthesis of the frst 20 years of pet and fmri studies of heard speech, spoken language and reading. Neuroimage 62(2), 816–847 (2012).13. Hulten, A., Vihla, M., Laine, M. & Salmelin, R. Accessing newly learned names and meanings in the native language. Human brain mapping 30(3), 976–989 (2009).14. Liljeström, M., Hulten, A., Parkkonen, L. & Salmelin, R. Comparing meg and fmri views to naming actions and objects. Human brain mapping 30(6), 1845–1856 (2009).15. Maess, B., Friederici, A. D., Damian, M., Meyer, A. S. & Levelt, W. J. Semantic category interference in overt picture naming: Sharpening current density localization by pca. Journal of cognitive neuroscience 14(3), 455–462 (2002).16. Salmelin, R., Hari, R., Lounasmaa, O. & Sams, M. Dynamics of brain activation during picture naming. Nature 368(6470), 463–465 (1994).17. Sörös, P., Cornelissen, K., Laine, M. & Salmelin, R. Naming actions and objects: cortical dynamics in healthy adults and in an anomic patient with a dissociation in action/object naming. Neuroimage 19(4), 1787–1801 (2003).18. Vihla, M., Laine, M. & Salmelin, R. Cortical dynamics of visual/semantic vs. phonological analysis in picture confrontation. Neuroimage 33(2), 732–738 (2006).19. Indefrey, P. On putative shortcomings and dangerous future avenues: response to strijkers & costa. Language, Cognition and Neuroscience 31(4), 517–520 (2016).20. Fargier, R. & Laganaro, M. Spatio-temporal dynamics of referential and inferential naming: Diferent brain and cognitive operations to lexical selection. Brain Topography 30(jun 2), 182–197 (2016).21. Kober, H. et al. New approach to localize speech relevant brain areas and hemispheric dominance using spatially filtered magnetoencephalography. Human brain mapping 14(4), 236–250 (2001).22. Miozzo, M., Pulvermüller, F. & Hauk, O. Early parallel activation of semantics and phonology in picture naming: Evidence from a multiple linear regression meg study. Cerebral Cortex 25(10), 3343–3355 (2014).23. Munding, D., Dubarry, A.-S. & Alario, F.-X. On the cortical dynamics of word production: A review of the meg evidence. Language, Cognition and Neuroscience 31(4), 441–462 (2016).24. Rahman, R. A. & Sommer, W. Does phonological encoding in speech production always follow the retrieval of semantic knowledge?: Electrophysiological evidence for parallel processing. Cognitive Brain Research 16(3), 372–382 (2003).25. Strijkers, K. & Costa, A. Te cortical dynamics of speaking: Present shortcomings and future avenues. Language, Cognition and Neuroscience 31(4), 484–503 (2016).26. Hämäläinen, M., Hari, R., Ilmoniemi, R. J., Knuutila, J. & Lounasmaa, O. V. Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain. Reviews of modern Physics 65(2), 413 (1993).27. Delorme, A., Palmer, J., Onton, J., Oostenveld, R. & Makeig, S. Independent eeg sources are dipolar. PloS one 7(2), e30135 (2012).28. DiRusso, F., Martínez, A., Sereno, M. I., Pitzalis, S. & Hillyard, S. A. Cortical sources of the early components of the visual evoked potential. Human brain mapping 15(2), 95–111 (2002).29. Riès, S., Janssen, N., Burle, B. & Alario, F.-X. Response-locked brain dynamics of word production. PLoS One 8(3), e58197 (2013).30. Glaser, W. R. Picture naming. Cognition 42(1), 61–105 (1992).31. Handy, T. C. Event-related potentials: A methods handbook. MIT press, (2005).32. Costa, A., Strijkers, K., Martin, C. & Tierry, G. Te time course of word retrieval revealed by event-related brain potentials during overt speech. Proceedings of the National Academy of Sciences 106(50), 21442–21446 (2009).33. Szekely, A. et al. A new on-line resource for psycholinguistic studies. Journal of memory and language 51(2), 247–250 (2004).34. Delorme, A. & Makeig, S. Eeglab: an open source toolbox for analysis of single-trial eeg dynamics including independent component analysis. Journal of neuroscience methods 134(1), 9–21 (2004).35. Winkler, I., Debener, S., Müller, K.-R. & Tangermann, M. On the infuence of high-pass fltering on ica-based artifact reduction in eeg-erp. In Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, pages 4101–4105. IEEE, (2015).36. Acunzo, D. J., MacKenzie, G. & van Rossum, M. C. Systematic biases in early erp and erf components as a result of high-pass fltering. Journal of neuroscience methods 209(1), 212–218 (2012).37. Maess, B., Schröger, E. & Widmann, A. High-pass filters and baseline correction in m/eeg analysis. commentary on:how inappropriate high-pass flters can produce artefacts and incorrect conclusions in erp studies of language and cognition. Journal of neuroscience methods 266, 164–165 (2016).38. Tanner, D., Morgan-Short, K. & Luck, S. J. How inappropriate high-pass filters can produce artifactual effects and incorrect conclusions in erp studies of language and cognition. Psychophysiology 52(8), 997–1009 (2015).39. Widmann, A., Schröger, E. & Maess, B. Digital flter design for electrophysiological data-a practical approach. Journal of neuroscience methods 250, 34–46 (2015).40. Porcaro, C., Medaglia, M. T. & Krott, A. Removing speech artifacts from electroencephalographic recordings during overt picture naming. NeuroImage 105, 171–180 (2015).41. Comon, P. Independent component analysis, a new concept? Signal processing 36(3), 287–314 (1994).42. Petersen, K., Hansen, L. K., Kolenda, T., Rostrup, E. & Strother, S. On the independent components of functional neuroimages. In Tird international conference on independent component analysis and blind source separation, pages 615–620 (2000).43. Makeig, S. et al. Dynamic brain sources of visual evoked responses. Science 295(5555), 690–694 (2002).44. Hyvarinen, A. Fast and robust fxed-point algorithms for independent component analysis. IEEE transactions on Neural Networks 10(3), 626–634 (1999).45. Kovacevic, N. & McIntosh, A. R. Groupwise independent component decomposition of eeg data and partial least square analysis. Neuroimage 35(3), 1103–1112 (2007).46. Makeig, S. et al. Electroencephalographic brain dynamics following manually responded visual targets. PLoS biology 2(6), e176 (2004).47. Artoni, F., Delorme, A. & Makeig, S. Applying dimension reduction to EEG data by principal component analysis reduces the quality of its subsequent independent component decomposition. NeuroImage 175(jul), 176–187 (2018).48. Makeig, S., Debener, S., Onton, J. & Delorme, A. Mining event-related brain dynamics. Trends in cognitive sciences 8(5), 204–210 (2004).49. Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on mri scans into gyral based regions of interest. Neuroimage 31(3), 968–980 (2006).50. Delorme, A., Sejnowski, T. & Makeig, S. Enhanced detection of artifacts in eeg data using higher-order statistics and independent component analysis. Neuroimage 34(4), 1443–1449 (2007).51. Joyce, C. A., Gorodnitsky, I. F. & Kutas, M. Automatic removal of eye movement and blink artifacts from eeg data using blind component separation. Psychophysiology 41(2), 313–325 (2004).52. Weber, M. J. & Tompson-Schill, S. L. Functional neuroimaging can support causal claims about brain function. Journal of cognitive neuroscience 22(11), 2415–2416 (2010).53. Janssen, N., Hernández-Cabrera, J. A., van der Meij, M. & Barber, H. A. Tracking the time course of competition during word production: Evidence for a post-retrieval mechanism of confict resolution. Cerebral Cortex 25(9), 2960–2969 (2014).54. Guthrie, D. & Buchwald, J. S. Signifcance testing of diference potentials. Psychophysiology 28(2), 240–244 (1991).55. Protopapas, A. Check vocal: A program to facilitate checking the accuracy and response time of vocal responses from dmdx. Behavior Research Methods 39(4), 859–862 (2007).56. Delorme, A., Miyakoshi, M., Jung, T.-P. & Makeig, S. Grand average erp-image plotting and statistics: A method for comparing variability in event-related single-trial eeg activities across subjects and conditions. Journal of neuroscience methods 250, 3–6 (2015).57. Geranmayeh, F., Wise, R. J. S., Mehta, A. & Leech, R. Overlapping networks engaged during spoken language production and its cognitive control. Journal of Neuroscience 34(jun 26), 8728–8740 (2014).58. Riecker, A. et al. fmri reveals two distinct cerebral networks subserving speech motor control. Neurology 64(4), 700–706 (2005).59. Hassan, M. et al. Dynamic reorganization of functional brain networks during picture naming. Cortex 73, 276–288 (2015).60. Song, J. et al. Eeg source localization: sensor density and head surface coverage. Journal of neuroscience methods 256, 9–21 (2015).61. Cisek, P. & Kalaska, J. F. Neural mechanisms for interacting with a world full of action choices. Annual review of neuroscience 33, 269–298 (2010).62. Ledberg, A., Bressler, S. L., Ding, M., Coppola, R. & Nakamura, R. Large-scale visuomotor integration in the cerebral cortex. Cerebral cortex 17(1), 44–62 (2006).63. Liang, H., Bressler, S. L., Ding, M., Truccolo, W. A. & Nakamura, R. Synchronized activity in prefrontal cortex during anticipation of visuomotor processing. Neuroreport 13(16), 2011–2015 (2002).64. Fetz, E. E. Are movement parameters recognizably coded in the activity of single neurons? Behavioral and Brain Sciences 15, 679–690 (1992).65. Smith, S. M. et al. Functional connectomics from resting-state fmri. Trends in cognitive sciences 17(12), 666–682 (2013).66. Fries, P. A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends in Cognitive Sciences 9(oct 10), 474–480 (2005).67. Kornhuber, H. H. & Deecke, L. Brain potential changes in voluntary and passive movements in humans: readiness potential and reaferent potentials. Pfügers Archiv-European Journal of Physiology 468(7), 1115–1124 (2016).68. Nickerson, L. D., Smith, S. M., Öngür, D. & Beckmann, C. F. Using dual regression to investigate network shape and amplitude in functional connectivity analyses. 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