How do reference montage and electrodes setup affect the measured scalp EEG potentials?

Objective. Human scalp electroencephalogram (EEG) is widely applied in cognitive neuroscience and clinical studies due to its non-invasiveness and ultra-high time resolution. However, the representativeness of the measured EEG potentials for the underneath neural activities is still a problem under...

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Published inJournal of neural engineering Vol. 15; no. 2; pp. 26013 - 26025
Main Authors Hu, Shiang, Lai, Yongxiu, Valdes-Sosa, Pedro A, Bringas-Vega, Maria L, Yao, Dezhong
Format Journal Article
LanguageEnglish
Published England IOP Publishing 26.01.2018
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ISSN1741-2560
1741-2552
1741-2552
DOI10.1088/1741-2552/aaa13f

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Abstract Objective. Human scalp electroencephalogram (EEG) is widely applied in cognitive neuroscience and clinical studies due to its non-invasiveness and ultra-high time resolution. However, the representativeness of the measured EEG potentials for the underneath neural activities is still a problem under debate. This study aims to investigate systematically how both reference montage and electrodes setup affect the accuracy of EEG potentials. Approach. First, the standard EEG potentials are generated by the forward calculation with a single dipole in the neural source space, for eleven channel numbers (10, 16, 21, 32, 64, 85, 96, 128, 129, 257, 335). Here, the reference is the ideal infinity implicitly determined by forward theory. Then, the standard EEG potentials are transformed to recordings with different references including five mono-polar references (Left earlobe, Fz, Pz, Oz, Cz), and three re-references (linked mastoids (LM), average reference (AR) and reference electrode standardization technique (REST)). Finally, the relative errors between the standard EEG potentials and the transformed ones are evaluated in terms of channel number, scalp regions, electrodes layout, dipole source position and orientation, as well as sensor noise and head model. Main results. Mono-polar reference recordings are usually of large distortions; thus, a re-reference after online mono-polar recording should be adopted in general to mitigate this effect. Among the three re-references, REST is generally superior to AR for all factors compared, and LM performs worst. REST is insensitive to head model perturbation. AR is subject to electrodes coverage and dipole orientation but no close relation with channel number. Significance. These results indicate that REST would be the first choice of re-reference and AR may be an alternative option for high level sensor noise case. Our findings may provide the helpful suggestions on how to obtain the EEG potentials as accurately as possible for cognitive neuroscientists and clinicians.
AbstractList Human scalp electroencephalogram (EEG) is widely applied in cognitive neuroscience and clinical studies due to its non-invasiveness and ultra-high time resolution. However, the representativeness of the measured EEG potentials for the underneath neural activities is still a problem under debate. This study aims to investigate systematically how both reference montage and electrodes setup affect the accuracy of EEG potentials.OBJECTIVEHuman scalp electroencephalogram (EEG) is widely applied in cognitive neuroscience and clinical studies due to its non-invasiveness and ultra-high time resolution. However, the representativeness of the measured EEG potentials for the underneath neural activities is still a problem under debate. This study aims to investigate systematically how both reference montage and electrodes setup affect the accuracy of EEG potentials.First, the standard EEG potentials are generated by the forward calculation with a single dipole in the neural source space, for eleven channel numbers (10, 16, 21, 32, 64, 85, 96, 128, 129, 257, 335). Here, the reference is the ideal infinity implicitly determined by forward theory. Then, the standard EEG potentials are transformed to recordings with different references including five mono-polar references (Left earlobe, Fz, Pz, Oz, Cz), and three re-references (linked mastoids (LM), average reference (AR) and reference electrode standardization technique (REST)). Finally, the relative errors between the standard EEG potentials and the transformed ones are evaluated in terms of channel number, scalp regions, electrodes layout, dipole source position and orientation, as well as sensor noise and head model.APPROACHFirst, the standard EEG potentials are generated by the forward calculation with a single dipole in the neural source space, for eleven channel numbers (10, 16, 21, 32, 64, 85, 96, 128, 129, 257, 335). Here, the reference is the ideal infinity implicitly determined by forward theory. Then, the standard EEG potentials are transformed to recordings with different references including five mono-polar references (Left earlobe, Fz, Pz, Oz, Cz), and three re-references (linked mastoids (LM), average reference (AR) and reference electrode standardization technique (REST)). Finally, the relative errors between the standard EEG potentials and the transformed ones are evaluated in terms of channel number, scalp regions, electrodes layout, dipole source position and orientation, as well as sensor noise and head model.Mono-polar reference recordings are usually of large distortions; thus, a re-reference after online mono-polar recording should be adopted in general to mitigate this effect. Among the three re-references, REST is generally superior to AR for all factors compared, and LM performs worst. REST is insensitive to head model perturbation. AR is subject to electrodes coverage and dipole orientation but no close relation with channel number.MAIN RESULTSMono-polar reference recordings are usually of large distortions; thus, a re-reference after online mono-polar recording should be adopted in general to mitigate this effect. Among the three re-references, REST is generally superior to AR for all factors compared, and LM performs worst. REST is insensitive to head model perturbation. AR is subject to electrodes coverage and dipole orientation but no close relation with channel number.These results indicate that REST would be the first choice of re-reference and AR may be an alternative option for high level sensor noise case. Our findings may provide the helpful suggestions on how to obtain the EEG potentials as accurately as possible for cognitive neuroscientists and clinicians.SIGNIFICANCEThese results indicate that REST would be the first choice of re-reference and AR may be an alternative option for high level sensor noise case. Our findings may provide the helpful suggestions on how to obtain the EEG potentials as accurately as possible for cognitive neuroscientists and clinicians.
Human scalp electroencephalogram (EEG) is widely applied in cognitive neuroscience and clinical studies due to its non-invasiveness and ultra-high time resolution. However, the representativeness of the measured EEG potentials for the underneath neural activities is still a problem under debate. This study aims to investigate systematically how both reference montage and electrodes setup affect the accuracy of EEG potentials.OBJECTIVEHuman scalp electroencephalogram (EEG) is widely applied in cognitive neuroscience and clinical studies due to its non-invasiveness and ultra-high time resolution. However, the representativeness of the measured EEG potentials for the underneath neural activities is still a problem under debate. This study aims to investigate systematically how both reference montage and electrodes setup affect the accuracy of EEG potentials.First, the standard EEG potentials are generated by the forward calculation with a single dipole in the neural source space, for eleven channel numbers (10, 16, 21, 32, 64, 85, 96, 128, 129, 257, 335). Here, the reference is the ideal infinity implicitly determined by forward theory. Then, the standard EEG potentials are transformed to recordings with different references including five monopolar references (Left earlobe, Fz, Pz, Oz, Cz), and three re-references (Linked Mastoids (LM), Average Reference (AR) and Reference Electrode Standardization Technique (REST)). Finally, the relative errors between the standard EEG potentials and the transformed ones are evaluated in terms of channel number, scalp regions, electrodes layout, dipole source position and orientation, as well as sensor noise and head model.APPROACHFirst, the standard EEG potentials are generated by the forward calculation with a single dipole in the neural source space, for eleven channel numbers (10, 16, 21, 32, 64, 85, 96, 128, 129, 257, 335). Here, the reference is the ideal infinity implicitly determined by forward theory. Then, the standard EEG potentials are transformed to recordings with different references including five monopolar references (Left earlobe, Fz, Pz, Oz, Cz), and three re-references (Linked Mastoids (LM), Average Reference (AR) and Reference Electrode Standardization Technique (REST)). Finally, the relative errors between the standard EEG potentials and the transformed ones are evaluated in terms of channel number, scalp regions, electrodes layout, dipole source position and orientation, as well as sensor noise and head model.Mono-polar reference recordings are usually of large distortions; thus, a re-reference after online mono-polar recording should be adopted in general to mitigate this effect. Among the three re-references, REST is generally superior to AR for all factors compared, and LM performs worst. REST is insensitive to head model perturbation. AR is subject to electrodes coverage and dipole orientation but no close relation with channel number.MAIN RESULTSMono-polar reference recordings are usually of large distortions; thus, a re-reference after online mono-polar recording should be adopted in general to mitigate this effect. Among the three re-references, REST is generally superior to AR for all factors compared, and LM performs worst. REST is insensitive to head model perturbation. AR is subject to electrodes coverage and dipole orientation but no close relation with channel number.These results indicate that REST would be the first choice of re-reference and AR may be an alternative option for high level sensor noise case. Our findings may provide the helpful suggestions on how to obtain the EEG potentials as accurately as possible for cognitive neuroscientists and clinicians.SIGNIFICANCEThese results indicate that REST would be the first choice of re-reference and AR may be an alternative option for high level sensor noise case. Our findings may provide the helpful suggestions on how to obtain the EEG potentials as accurately as possible for cognitive neuroscientists and clinicians.
Objective. Human scalp electroencephalogram (EEG) is widely applied in cognitive neuroscience and clinical studies due to its non-invasiveness and ultra-high time resolution. However, the representativeness of the measured EEG potentials for the underneath neural activities is still a problem under debate. This study aims to investigate systematically how both reference montage and electrodes setup affect the accuracy of EEG potentials. Approach. First, the standard EEG potentials are generated by the forward calculation with a single dipole in the neural source space, for eleven channel numbers (10, 16, 21, 32, 64, 85, 96, 128, 129, 257, 335). Here, the reference is the ideal infinity implicitly determined by forward theory. Then, the standard EEG potentials are transformed to recordings with different references including five mono-polar references (Left earlobe, Fz, Pz, Oz, Cz), and three re-references (linked mastoids (LM), average reference (AR) and reference electrode standardization technique (REST)). Finally, the relative errors between the standard EEG potentials and the transformed ones are evaluated in terms of channel number, scalp regions, electrodes layout, dipole source position and orientation, as well as sensor noise and head model. Main results. Mono-polar reference recordings are usually of large distortions; thus, a re-reference after online mono-polar recording should be adopted in general to mitigate this effect. Among the three re-references, REST is generally superior to AR for all factors compared, and LM performs worst. REST is insensitive to head model perturbation. AR is subject to electrodes coverage and dipole orientation but no close relation with channel number. Significance. These results indicate that REST would be the first choice of re-reference and AR may be an alternative option for high level sensor noise case. Our findings may provide the helpful suggestions on how to obtain the EEG potentials as accurately as possible for cognitive neuroscientists and clinicians.
Human scalp electroencephalogram (EEG) is widely applied in cognitive neuroscience and clinical studies due to its non-invasiveness and ultra-high time resolution. However, the representativeness of the measured EEG potentials for the underneath neural activities is still a problem under debate. This study aims to investigate systematically how both reference montage and electrodes setup affect the accuracy of EEG potentials. First, the standard EEG potentials are generated by the forward calculation with a single dipole in the neural source space, for eleven channel numbers (10, 16, 21, 32, 64, 85, 96, 128, 129, 257, 335). Here, the reference is the ideal infinity implicitly determined by forward theory. Then, the standard EEG potentials are transformed to recordings with different references including five monopolar references (Left earlobe, Fz, Pz, Oz, Cz), and three re-references (Linked Mastoids (LM), Average Reference (AR) and Reference Electrode Standardization Technique (REST)). Finally, the relative errors between the standard EEG potentials and the transformed ones are evaluated in terms of channel number, scalp regions, electrodes layout, dipole source position and orientation, as well as sensor noise and head model. Mono-polar reference recordings are usually of large distortions; thus, a re-reference after online mono-polar recording should be adopted in general to mitigate this effect. Among the three re-references, REST is generally superior to AR for all factors compared, and LM performs worst. REST is insensitive to head model perturbation. AR is subject to electrodes coverage and dipole orientation but no close relation with channel number. These results indicate that REST would be the first choice of re-reference and AR may be an alternative option for high level sensor noise case. Our findings may provide the helpful suggestions on how to obtain the EEG potentials as accurately as possible for cognitive neuroscientists and clinicians.
Author Hu, Shiang
Valdes-Sosa, Pedro A
Bringas-Vega, Maria L
Yao, Dezhong
Lai, Yongxiu
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Cites_doi 10.1016/j.neuroimage.2010.07.033
10.1186/1475-925X-9-45
10.1176/appi.ajp.162.3.459
10.1093/acprof:oso/9780195050387.001.0001
10.1007/s10548-016-0543-x
10.1088/0967-3334/22/4/305
10.1016/j.neuroimage.2006.09.024
10.1016/0013-4694(58)90053-1
10.1016/S1388-2457(02)00337-1
10.1007/7657_2013_65
10.1385/NI:3:4:315
10.1016/0168-5597(85)90058-9
10.1016/0013-4694(93)90121-B
10.1111/1469-8986.3850847
10.1016/S1388-2457(99)00205-9
10.1016/0013-4694(71)90165-9
10.1016/j.clinph.2010.04.030
10.3389/fnins.2017.00601
10.1016/j.neuroimage.2007.02.034
10.1186/1743-0003-5-25
10.1016/0168-5597(93)90043-O
10.1016/S0013-4694(97)00106-5
10.1155/2011/923703
10.1007/BF01135568
10.1016/S1388-2457(00)00527-7
10.1023/A:1014590923185
10.1063/1.341983
10.1016/S0013-4694(97)00066-7
10.1006/nimg.2001.0825
10.1088/0967-3334/26/3/003
10.1186/1743-0003-4-46
10.1111/j.1469-8986.1993.tb02081.x
10.1016/0013-4694(50)90040-X
10.4249/scholarpedia.7632
10.1088/1741-2560/13/3/036016
10.1109/TMI.2004.837363
10.1007/BF02523206
10.1109/51.646230
10.1080/00029238.1985.11080163
10.1109/10.686789
10.1088/1741-2560/12/5/056012
10.1016/S1053-8119(09)70884-5
10.1155/2011/879716
10.1088/0266-5611/20/4/007
10.3389/fnins.2017.00262
10.1016/0013-4694(58)90081-6
10.1016/0013-4694(65)90195-1
10.3389/fnins.2017.00205
10.1016/j.cmpb.2004.07.002
10.1007/s10548-012-0261-y
10.1016/j.clinph.2005.08.007
10.1016/j.neuroimage.2014.08.056
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Issue 2
Keywords Electrode Layout
Reference Montage
Channel Number
Potential Relative Error
Language English
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References 44
45
46
47
48
Wolters C H (33) 2004; 20
Chella F (16) 2016; 13
Chatrian G E (22) 1985; 25
50
Yao D (49) 2005; 26
51
52
53
10
54
55
12
13
14
17
18
19
1
2
3
5
6
8
9
20
21
24
25
26
27
28
Christodoulakis M (36) 2013
29
Scherg M (7) 1990; 6
30
31
32
34
35
37
38
39
Liu Q (15) 2015; 12
Pascual-Marqui R D (4) 1999; 1
Michel C M (23) 2004; 8
Yao D (11) 2001; 22
40
41
42
43
References_xml – ident: 27
  doi: 10.1016/j.neuroimage.2010.07.033
– volume: 6
  start-page: 40
  year: 1990
  ident: 7
  publication-title: Audit. Evoked Magn. Fields Electr. Potentials Adv. Audiol.
– ident: 45
  doi: 10.1186/1475-925X-9-45
– ident: 31
  doi: 10.1176/appi.ajp.162.3.459
– ident: 42
  doi: 10.1093/acprof:oso/9780195050387.001.0001
– ident: 43
  doi: 10.1007/s10548-016-0543-x
– volume: 22
  start-page: 693
  issn: 0967-3334
  year: 2001
  ident: 11
  publication-title: Physiol. Meas.
  doi: 10.1088/0967-3334/22/4/305
– ident: 19
  doi: 10.1016/j.neuroimage.2006.09.024
– ident: 20
  doi: 10.1016/0013-4694(58)90053-1
– ident: 52
  doi: 10.1016/S1388-2457(02)00337-1
– start-page: 103
  year: 2013
  ident: 36
  publication-title: Epilepsy Modern Electroencephalographic Assessment Techniques: Theory and Applications
  doi: 10.1007/7657_2013_65
– ident: 40
  doi: 10.1385/NI:3:4:315
– ident: 41
  doi: 10.1016/0168-5597(85)90058-9
– ident: 24
  doi: 10.1016/0013-4694(93)90121-B
– ident: 55
  doi: 10.1111/1469-8986.3850847
– ident: 25
  doi: 10.1016/S1388-2457(99)00205-9
– ident: 2
  doi: 10.1016/0013-4694(71)90165-9
– ident: 38
  doi: 10.1016/j.clinph.2010.04.030
– ident: 44
  doi: 10.3389/fnins.2017.00601
– ident: 51
  doi: 10.1016/j.neuroimage.2007.02.034
– ident: 5
  doi: 10.1186/1743-0003-5-25
– ident: 34
  doi: 10.1016/0168-5597(93)90043-O
– ident: 21
  doi: 10.1016/S0013-4694(97)00106-5
– ident: 29
  doi: 10.1155/2011/923703
– ident: 6
  doi: 10.1007/BF01135568
– ident: 18
  doi: 10.1016/S1388-2457(00)00527-7
– ident: 47
  doi: 10.1023/A:1014590923185
– volume: 8
  start-page: 1
  year: 2004
  ident: 23
  publication-title: Electr. Geod. Inc.
– ident: 32
  doi: 10.1063/1.341983
– ident: 8
  doi: 10.1016/S0013-4694(97)00066-7
– ident: 1
  doi: 10.1006/nimg.2001.0825
– volume: 26
  start-page: 173
  issn: 0967-3334
  year: 2005
  ident: 49
  publication-title: Physiol. Meas.
  doi: 10.1088/0967-3334/26/3/003
– ident: 46
  doi: 10.1186/1743-0003-4-46
– ident: 54
  doi: 10.1111/j.1469-8986.1993.tb02081.x
– ident: 10
  doi: 10.1016/0013-4694(50)90040-X
– ident: 3
  doi: 10.4249/scholarpedia.7632
– volume: 13
  start-page: 36016
  issn: 1741-2552
  year: 2016
  ident: 16
  publication-title: J. Neural Eng.
  doi: 10.1088/1741-2560/13/3/036016
– ident: 28
  doi: 10.1109/TMI.2004.837363
– ident: 50
  doi: 10.1007/BF02523206
– ident: 35
  doi: 10.1109/51.646230
– volume: 25
  start-page: 83
  issn: 0002-9238
  year: 1985
  ident: 22
  publication-title: Am. J. EEG Technol.
  doi: 10.1080/00029238.1985.11080163
– ident: 39
  doi: 10.1109/10.686789
– volume: 12
  start-page: 56012
  issn: 1741-2552
  year: 2015
  ident: 15
  publication-title: J. Neural Eng.
  doi: 10.1088/1741-2560/12/5/056012
– ident: 26
  doi: 10.1016/S1053-8119(09)70884-5
– ident: 30
  doi: 10.1155/2011/879716
– volume: 20
  start-page: 1099
  issn: 0266-5611
  year: 2004
  ident: 33
  publication-title: Inverse Probl.
  doi: 10.1088/0266-5611/20/4/007
– ident: 14
  doi: 10.3389/fnins.2017.00262
– ident: 9
  doi: 10.1016/0013-4694(58)90081-6
– volume: 1
  start-page: 75
  year: 1999
  ident: 4
  publication-title: Int. J. Bioelectromagn.
– ident: 37
  doi: 10.1016/0013-4694(65)90195-1
– ident: 17
  doi: 10.3389/fnins.2017.00205
– ident: 12
  doi: 10.1016/j.cmpb.2004.07.002
– ident: 13
  doi: 10.1007/s10548-012-0261-y
– ident: 53
  doi: 10.1016/j.clinph.2005.08.007
– ident: 48
  doi: 10.1016/j.neuroimage.2014.08.056
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Snippet Objective. Human scalp electroencephalogram (EEG) is widely applied in cognitive neuroscience and clinical studies due to its non-invasiveness and ultra-high...
Human scalp electroencephalogram (EEG) is widely applied in cognitive neuroscience and clinical studies due to its non-invasiveness and ultra-high time...
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SubjectTerms channel number
electrode layout
potential relative error
reference montage
Title How do reference montage and electrodes setup affect the measured scalp EEG potentials?
URI https://iopscience.iop.org/article/10.1088/1741-2552/aaa13f
https://www.ncbi.nlm.nih.gov/pubmed/29235448
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Volume 15
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