Brain Topography

, Volume 31, Issue 2, pp 174–185 | Cite as

A Preliminary Study on Precision Image Guidance for Electrode Placement in an EEG Study

  • Sangseo Jeon
  • Jongho Chien
  • Chanho Song
  • Jaesung Hong
Original Paper


Conventional methods for positioning electroencephalography electrodes according to the international 10/20 system are based on the manual identification of the principal 10/20 landmarks via visual inspection and palpation, inducing intersession variations in their determined locations due to structural ambiguity or poor visibility. To address the variation issue, we propose an image guidance system for precision electrode placement. Following the electrode placement according to the 10/20 system, affixed electrodes are laser-scanned together with the facial surface. For subsequent procedures, the laser scan is conducted likewise after positioning the electrodes in an arbitrary manner, and following the measurement of fiducial electrode locations, frame matching is performed to determine a transformation from the coordinate frame of the position tracker to that of the laser-scanned image. Finally, by registering the intra-procedural scan of the facial surface to the reference scan, the current tracking data of the electrodes can be visualized relative to the reference goal positions without manually measuring the four principal landmarks for each trial. The experimental results confirmed that use of the electrode navigation system significantly improved the electrode placement precision compared to the conventional 10/20 system (p < 0.005). The proposed system showed the possibility of precise image-guided electrode placement as an alternative to the conventional manual 10/20 system.


Electroencephalography (EEG) EEG electrode placement International 10/20 system Landmark identification Registration Navigation 



This work was supported by the DGIST R&D Program of the Ministry of Science, ICT and Future Planning (17-BD-0401). Part of this study was reported at the Asian Conference on Computer Aided Surgery held in Singapore in 2015, at the International IEEE EMBS Conference on Neural Engineering held in Montpellier France in 2015 and at the 30th International Congress of Computer Assisted Radiology and Surgery held in Heidelberg Germany in 2016, respectively. The authors thank Hyunseok Choi, a PhD candidate from DGIST, for his valuable help on software programming.


This study was funded by the DGIST R&D Program of the Ministry of Science, ICT and Future Planning (17-BD-0401).

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

For this type of study, formal approval is not required.

Informed Consent

Informed consent was obtained from all individual participants included in the study.


  1. Arata J, Kozuka H, Kim HW et al (2010) Open core control software for surgical robots. Int J Comput Assist Radiol Surg 5:211–220CrossRefPubMedGoogle Scholar
  2. Arun KS, Huang TS, Blostein SD (1987) Least-squares fitting of two 3-D point sets. IEEE Trans Pattern Anal Mach Intell 9:698–700CrossRefPubMedGoogle Scholar
  3. Astolfi L, Cincotti F, Mattia D et al (2007) Comparison of different cortical connectivity estimators for high-resolution EEG recordings. Hum Brain Mapp 28:143–157CrossRefPubMedGoogle Scholar
  4. Atcherson SR, Gould HJ, Pousson MA, Prout TM (2007) Variability of electrode positions using electrode caps. Brain Topogr 20:105–111CrossRefPubMedGoogle Scholar
  5. Baysal U, Şengül G (2010) Single camera photogrammetry system for EEG electrode identification and localization. Ann Biomed Eng 38:1539–1547CrossRefPubMedGoogle Scholar
  6. Besl PJ, McKay ND (1992) Method for registration of 3-D shapes. In: Robotics-DL tentative. pp 586–606Google Scholar
  7. Böcker KBE, van Avermaete JAG, van den Berg-Lenssen MMC (1994) The international 10–20 system revisited: Cartesian and spherical co-ordinates. Brain Topogr 6:231–235CrossRefPubMedGoogle Scholar
  8. Bradski G, Kaehler A (2008) Learning OpenCV: Computer vision with the OpenCV library. O’Reilly Media, Inc., FarnhamGoogle Scholar
  9. Bulea TC, Kim J, Damiano DL et al (2015) Prefrontal, posterior parietal and sensorimotor network activity underlying speed control during walking. Front Hum Neurosci 9:247CrossRefPubMedPubMedCentralGoogle Scholar
  10. Chatrian GE, Lettich E, Nelson PL (1985) Ten percent electrode system for topographic studies of spontaneous and evoked EEG activities. Am J EEG Technol 25:83–92Google Scholar
  11. Chatrian GE, Lettich E, Nelson PL (1988) Modified Nomenclature for the “10%” Electrode System1. J Clin Neurophysiol 5:183–186CrossRefPubMedGoogle Scholar
  12. Chen Y, Medioni G (1992) Object modelling by registration of multiple range images. Image Vis Comput 10:145–155CrossRefGoogle Scholar
  13. Cho B, Oka M, Matsumoto N et al (2013) Warning navigation system using real-time safe region monitoring for otologic surgery. Int J Comput Assist Radiol Surg 8:395–405CrossRefPubMedGoogle Scholar
  14. Coben LA, Danziger W, Storandt M (1985) A longitudinal EEG study of mild senile dementia of Alzheimer type: changes at 1 year and at 2.5 years. Electroencephalogr Clin Neurophysiol 61:101–112CrossRefPubMedGoogle Scholar
  15. Committee EPN others (1994) Guideline thirteen: guidelines for standard electrode position nomenclature. J Clin Neurophysiol 11:111–113CrossRefGoogle Scholar
  16. Cutini S, Scatturin P, Zorzi M (2011) A new method based on ICBM152 head surface for probe placement in multichannel fNIRS. Neuroimage 54:919–927CrossRefPubMedGoogle Scholar
  17. De Munck JC, Vijn PCM, Spekreijse H (1991) A practical method for determining electrode positions on the head. Electroencephalogr Clin Neurophysiol 78:85–87CrossRefPubMedGoogle Scholar
  18. Deonna T, Zesiger P, Davidoff V et al (2000) Benign partial epilepsy of childhood: a longitudinal neuropsychological and EEG study of cognitive function. Dev Med Child Neurol 42:595–603CrossRefPubMedGoogle Scholar
  19. Echallier JF, Perrin F, Pernier J (1992) Computer-assisted placement of electrodes on the human head. Electroencephalogr Clin Neurophysiol 82:160–163CrossRefPubMedGoogle Scholar
  20. Eddelbuettel D, Sanderson C (2014) RcppArmadillo: accelerating R with high-performance C + + linear algebra. Comput Stat Data Anal 71:1054–1063CrossRefGoogle Scholar
  21. Egger J, Tokuda J, Chauvin L et al (2012) Integration of the OpenIGTLink Network Protocol for image-guided therapy with the medical platform MeVisLab. Int J Med Robot Comput Assist Surg 8:282–290CrossRefGoogle Scholar
  22. Elhawary H, Liu H, Patel P et al (2011) Intra-operative real-time querying of white matter tracts during frameless stereotactic neuronavigation. Neurosurgery 68:506CrossRefPubMedPubMedCentralGoogle Scholar
  23. Figueiredo CP, Dias NS, Hoffmann K-P, Mendes PM (2008) 3D electrode localization on wireless sensor networks for wearable BCI. In: Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE. pp 2365–2368Google Scholar
  24. Giacometti P, Diamond SG (2013) Compliant head probe for positioning electroencephalography electrodes and near-infrared spectroscopy optodes. J Biomed Opt 18:27005CrossRefPubMedGoogle Scholar
  25. He P, Estepp JR (2013) A practical method for quickly determining electrode positions in high-density EEG studies. Neurosci Lett 541:73–76CrossRefPubMedGoogle Scholar
  26. Herwig U, Satrapi P, Schonfeldt-Lecuona C (2003) Using the international 10–20 EEG system for positioning of transcranial magnetic stimulation. Brain Topogr 16:95–99CrossRefPubMedGoogle Scholar
  27. Hong J, Hashizume M (2010) An effective point-based registration tool for surgical navigation. Surg Endosc 24:944–948CrossRefPubMedGoogle Scholar
  28. Horn BKP (1987) Closed-form solution of absolute orientation using unit quaternions. JOSA A 4:629–642CrossRefGoogle Scholar
  29. Jasper H (1958) Report of the committee on methods of clinical examination in electroencephalography. Electroencephalogr Clin Neurophysiol 10:370–375CrossRefGoogle Scholar
  30. Jeon S, Lee GW, Jeon YD et al (2015) A preliminary study on surgical navigation for epiduroscopic laser neural decompression. Proc Inst Mech Eng H 229:693–v702CrossRefPubMedGoogle Scholar
  31. Jurcak V, Okamoto M, Singh A, Dan I (2005) Virtual 10–20 measurement on MR images for inter-modal linking of transcranial and tomographic neuroimaging methods. Neuroimage 26:1184–1192CrossRefPubMedGoogle Scholar
  32. Jurcak V, Tsuzuki D, Dan I (2007) 10/20, 10/10, and 10/5 systems revisited: their validity as relative head-surface-based positioning systems. Neuroimage 34:1600–1611CrossRefPubMedGoogle Scholar
  33. Kähkönen S, Kesäniemi M, Nikouline VV et al (2001) Ethanol modulates cortical activity: direct evidence with combined TMS and EEG. Neuroimage 14:322–328CrossRefPubMedGoogle Scholar
  34. Kavanagh KT, Clark ST (1989) Comparison of the mastoid to vertex and mastoid to high forehead electrode arrays in recording auditory evoked potentials. Ear Hear 10:259–261CrossRefPubMedGoogle Scholar
  35. Kim S, Hong J, Joung S et al (2011) Dual surgical navigation using augmented and virtual environment techniques. Int J Optomechatronics 5:155–169CrossRefGoogle Scholar
  36. Klem GH, Luders HO, Jasper HH et al (1999) The ten-twenty electrode system of the International Federation. Electroencephalogr Clin Neurophysiol 52:3–6Google Scholar
  37. Koessler L, Maillard L, Benhadid A et al (2007) Spatial localization of EEG electrodes. Neurophysiol Clin Neurophysiol 37:97–102CrossRefGoogle Scholar
  38. Koessler L, Benhadid A, Maillard L et al (2008) Automatic localization and labeling of EEG sensors (ALLES) in MRI volume. Neuroimage 41:914–923CrossRefPubMedGoogle Scholar
  39. Koessler L, Cecchin T, Ternisien E, Maillard L (2010) 3D handheld laser scanner based approach for automatic identification and localization of EEG sensors. In: Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE. pp 3707–3710Google Scholar
  40. Lagerlund TD, Sharbrough FW, Jack CR et al (1993) Determination of 10–20 system electrode locations using magnetic resonance image scanning with markers. Electroencephalogr Clin Neurophysiol 86:7–14CrossRefPubMedGoogle Scholar
  41. Le J, Lu M, Pellouchoud E, Gevins A (1998) A rapid method for determining standard 10/10 electrode positions for high resolution EEG studies. Electroencephalogr Clin Neurophysiol 106:554–558CrossRefPubMedGoogle Scholar
  42. Lepetit V, Moreno-Noguer F, Fua P (2009) Epnp: an accurate o (n) solution to the pnp problem. Int J Comput Vis 81:155CrossRefGoogle Scholar
  43. Matsumoto N, Oka M, Cho B et al (2012) Cochlear implantation assisted by noninvasive image guidance. Otol Neurotol 33:1333–1338CrossRefPubMedGoogle Scholar
  44. Munoz-Salinas R (2012) ARUCO: a minimal library for Augmented Reality applications based on OpenCvGoogle Scholar
  45. Myslobodsky MS, Coppola R, Bar-Ziv J, Weinberger DR (1990) Adequacy of the International 10–20 electrode system for computed neurophysiologic topography. J Clin Neurophysiol 7:507–518CrossRefPubMedGoogle Scholar
  46. Nuwer MR, Comi G, Emerson R et al (1998) IFCN standards for digital recording of clinical EEG. Electroencephalogr Clin Neurophysiol 106:259–261CrossRefPubMedGoogle Scholar
  47. Oostenveld R, Praamstra P (2001) The five percent electrode system for high-resolution EEG and ERP measurements. Clin Neurophysiol 112:713–719CrossRefPubMedGoogle Scholar
  48. Otten P, Kim J, Son SH (2015) A framework to automate assessment of upper-limb motor function impairment: A feasibility study. Sensors 15:20097–20114CrossRefPubMedPubMedCentralGoogle Scholar
  49. Qian S, Sheng Y (2011) A single camera photogrammetry system for multi-angle fast localization of EEG electrodes. Ann Biomed Eng 39:2844CrossRefPubMedGoogle Scholar
  50. Reis PMR, Lochmann M (2015) Using a motion capture system for spatial localization of EEG electrodes. Front Neurosci 9:130CrossRefPubMedPubMedCentralGoogle Scholar
  51. Richards JE, Boswell C, Stevens M, Vendemia JMC (2015) Evaluating methods for constructing average high-density electrode positions. Brain Topogr 28:70–86CrossRefPubMedGoogle Scholar
  52. Sanderson C et al (2010) Armadillo: an open source C + + linear algebra library for fast prototyping and computationally intensive experimentsGoogle Scholar
  53. Schwartz D, Lemoine D, Poiseau E, Barillot C (1996) Registration of MEG/EEG data with 3D MRI: methodology and precision issues. Brain Topogr 9:101–116CrossRefGoogle Scholar
  54. Sitaram R, Zhang H, Guan C et al (2007) Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain–computer interface. Neuroimage 34:1416–1427CrossRefPubMedGoogle Scholar
  55. Souzaki R, Kinoshita Y, Matsuura T et al (2011) Successful resection of an undifferentiated sarcoma in a child using a real-time surgical navigation system in an open magnetic resonance imaging operation room. J Pediatr Surg 46:608–611CrossRefPubMedGoogle Scholar
  56. Tauscher S, Tokuda J, Schreiber G et al (2015) OpenIGTLink interface for state control and visualisation of a robot for image-guided therapy systems. Int J Comput Assist Radiol Surg 10:285–292CrossRefPubMedGoogle Scholar
  57. Tokuda J, Fischer GS, Papademetris X et al (2009) OpenIGTLink: an open network protocol for image-guided therapy environment. Int J Med Robot Comput Assist Surg 5:423–434CrossRefGoogle Scholar
  58. Tokuda J, Fischer GS, DiMaio SP et al (2010) Integrated navigation and control software system for MRI-guided robotic prostate interventions. Comput Med Imaging Graph 34:3–8CrossRefPubMedGoogle Scholar
  59. Towle VL, Bolanos J, Suarez D et al (1993) The spatial location of EEG electrodes: locating the best-fitting sphere relative to cortical anatomy. Electroencephalogr Clin Neurophysiol 86:1–6CrossRefPubMedGoogle Scholar
  60. Tsutsumi N, Tomikawa M, Uemura M et al (2013) Image-guided laparoscopic surgery in an open MRI operating theater. Surg Endosc 27:2178–2184CrossRefPubMedGoogle Scholar
  61. Tsuzuki D, Watanabe H, Dan I, Taga G (2016) Minr 10/20 system: Quantitative and reproducible cranial landmark setting method for mri based on minimum initial reference points. J Neurosci Methods 264:86–93CrossRefPubMedGoogle Scholar
  62. Van Olphen AF, Rodenburg M, Verwey C (1978) Distribution of brain stem responses to acoustic stimuli over the human scalp. Audiology 17:511–518CrossRefPubMedGoogle Scholar
  63. Vaughan HG, Ritter W (1970) The sources of auditory evoked responses recorded from the human scalp. Electroencephalogr Clin Neurophysiol 28:360–367CrossRefPubMedGoogle Scholar
  64. Wilm J (2010) Iterative Closest Point. Accessed 27 Jun 2017
  65. Wood CC, Allison T (1981) Interpretation of evoked potentials: A neurophysiological perspective. Can J Psychol Can Psychol 35:113CrossRefGoogle Scholar
  66. Yoo S-S, Guttmann CRG, Ives JR et al (1997) 3D localization of surface 10–20 EEG electrodes on high resolution anatomical MR images. Electroencephalogr Clin Neurophysiol 102:335–339CrossRefPubMedGoogle Scholar
  67. Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22:1330–1334CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  • Sangseo Jeon
    • 1
  • Jongho Chien
    • 1
  • Chanho Song
    • 1
  • Jaesung Hong
    • 1
  1. 1.Department of Robotics EngineeringDGISTDaeguRepublic of Korea

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