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Brain Topography

, Volume 28, Issue 5, pp 647–656 | Cite as

Novel Multipin Electrode Cap System for Dry Electroencephalography

  • P. FiedlerEmail author
  • P. Pedrosa
  • S. Griebel
  • C. Fonseca
  • F. Vaz
  • E. Supriyanto
  • F. Zanow
  • J. Haueisen
Original Paper

Abstract

Current usage of electroencephalography (EEG) is limited to laboratory environments. Self-application of a multichannel wet EEG caps is practically impossible, since the application of state-of-the-art wet EEG sensors requires trained laboratory staff. We propose a novel EEG cap system with multipin dry electrodes overcoming this problem. We describe the design of a novel 24-pin dry electrode made from polyurethane and coated with Ag/AgCl. A textile cap system holds 97 of these dry electrodes. An EEG study with 20 volunteers compares the 97-channel dry EEG cap with a conventional 128-channel wet EEG cap for resting state EEG, alpha activity, eye blink artifacts and checkerboard pattern reversal visual evoked potentials. All volunteers report a good cap fit and good wearing comfort. Average impedances are below 150 kΩ for 92 out of 97 dry electrodes, enabling recording with standard EEG amplifiers. No significant differences are observed between wet and dry power spectral densities for all EEG bands. No significant differences are observed between the wet and dry global field power time courses of visual evoked potentials. The 2D interpolated topographic maps show significant differences of 3.52 and 0.44 % of the map areas for the N75 and N145 VEP components, respectively. For the P100 component, no significant differences are observed. Dry multipin electrodes integrated in a textile EEG cap overcome the principle limitations of wet electrodes, allow rapid application of EEG multichannel caps by non-trained persons, and thus enable new fields of application for multichannel EEG acquisition.

Keywords

Biopotential electrode EEG ECG Dry electrode 

Notes

Acknowledgments

This work was financially supported by the German Federal Ministry of Education and Research (03IPT605A), the German Academic Exchange Service (D/57036536), the Thüringer Aufbaubank and the European Social Fund (2012 FGR 0014), and the European Union (FP7-PEOPLE Marie Curie IAPP project 610950).

References

  1. Askamp J, van Putten MJ (2014) Mobile EEG in epilepsy. Int J Psychophysiol 91(1):30–35. doi: 10.1016/j.ijpsycho.2013.09.002 PubMedCrossRefGoogle Scholar
  2. Chi YM, Wang YT, Wang Y, Maier C, Jung TP, Cauwenberghs G (2012) Dry and noncontact EEG sensors for mobile brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng 20(2):228–235. doi: 10.1109/TNSRE.2011.2174652 PubMedCrossRefGoogle Scholar
  3. D’Angelo LT, Parlow J, Spiessl W, Hoch S, Lueth TC (2010) A system for unobtrusive in-car vital parameter acquisition and processing. In: Proceedings of the 4th international conference on pervasive computing technologies for healthcare 2010, Munich, Germany, New York, pp 1–7Google Scholar
  4. Daly JJ, Wolpaw JR (2008) Brain-computer interfaces in neurological rehabilitation. Lancet Neurol 7(11):1032–1043. doi: 10.1016/S1474-4422(08)70223-0 PubMedCrossRefGoogle Scholar
  5. De Vos M, Debener S (2014) Mobile EEG: towards brain activity monitoring during natural action and cognition. Int J Psychophysiol 91(1):1–2. doi: 10.1016/j.ijpsycho.2013.10.008 PubMedCrossRefGoogle Scholar
  6. Debener S, Minow F, Emkes R, Gandras K, De Vos M (2012) How about taking a low-cost, small, and wireless EEG for walk? Psychophysiology 49:1449–1453. doi: 10.1111/j.1469-8986.2012.01471.x CrossRefGoogle Scholar
  7. Di Fronso S, Bertollo M, Comani S (2015) Neural markers of performance states in an Olympic Athlete: a case study in air-pistol shooting. J Sport SciGoogle Scholar
  8. Fiedler P, Brodkorb S, Fonseca C, Vaz F, Zanow F, Haueisen J (2010) Novel TiN-based dry EEG electrodes: Influence of electrode shape and number on contact impedance and signal quality. In: Panagiotis DB, Pallikarakis N (eds) IFMBE proceedings of the xii mediterranean conference on medical and biological engineering and computing, 2010 May 27–30, Chalkidiki, Greece. Springer, Berlin, pp 418–421CrossRefGoogle Scholar
  9. Fiedler P, Haueisen J, Jannek D, Griebel S, Zentner L, Vaz F, Zanow F, Haueisen J (2014) Comparison of three types of dry electrodes for electroencephalography. Acta Imeko 3(3):33–37Google Scholar
  10. Fiedler P, Griebel S, Pedrosa P, Fonseca C, Vaz F, Zentner L, Zanow F, Haueisen J (2015) Multichannel EEG with novel Ti/TiN dry electrodes. Sens Actuator A 221:139–147. doi: 10.1016/j.sna.2014.10.010 CrossRefGoogle Scholar
  11. Graichen U, Eichardt R, Fiedler P, Strohmeier D, Zanow F, Haueisen J (2015) SPHARA—A generalized spatial Fourier analysis for multi-sensor systems with non-uniformly arranged sensors: application to EEG. PLoS OneGoogle Scholar
  12. Greischar LL, Burghy CA, van Reekum CM, Jackson DC, Pizzagalli DA, Mueller C, Davidson RJ (2004) Effects of electrode density and electrolyte spreading in dense array electroencephalographic recording. Clin Neurophysiol 115(3):710–720. doi: 10.1016/j.clinph.2003.10.028 PubMedCrossRefGoogle Scholar
  13. Hoddes E, Dement W, Zarcone V (1972) The development and use of the Stanford sleepiness scale (SSS). Psychophysiology 9:150Google Scholar
  14. Hwang HJ, Kim S, Choi S, Im CH (2013) EEG-based brain-computer interfaces: a thorough literature survey. Int J Hum Comput Interact 29(12):814–826. doi: 10.1080/10447318.2013.780869 CrossRefGoogle Scholar
  15. Jordan KG (2004) Emergency EEG and continuous EEG monitoring in acute ischemic stroke. J Clin Neurophysiol 21(5):341–352PubMedGoogle Scholar
  16. Junghöfer M, Elbert T, Tucker DM, Braun C (1999) The polar average reference effect: a bias in estimating the head surface integral in EEG recording. Clin Neurophysiol 110(6):1149–1155PubMedCrossRefGoogle Scholar
  17. Klem GH, Luders HO, Jasper HH, Elger C (1999) The ten-twenty electrode system ofthe International Federation. The International Federation of Clinical Neurophysiology. Electroencephalogr Clin Neurophysiol Suppl 52:3–6Google Scholar
  18. Lehmann D, Skrandies W (1984) Spatial analysis of evoked potentials in man—a review. Progr Neurobiol 23(3):227–250CrossRefGoogle Scholar
  19. Liao LD, Lin CT, McDowell K, Wickenden AE, Gramann K, Tzyy-Ping J, Li-Wei K, Jyh-Yeong C (2012) Biosensor technologies for augmented brain-computer interfaces in the next decades. Proc IEEE 100:1553–1566. doi: 10.1109/JPROC.2012.2184829 CrossRefGoogle Scholar
  20. Lopez-Gordo MA, Sanchez-Morillo D, Pelayo Valle F (2014) Dry EEG electrodes. Sensors 14(7):12847–12870. doi: 10.3390/s140712847 PubMedCentralPubMedCrossRefGoogle Scholar
  21. Michel V, Mazzola L, Lemesle M, Vercueil L (2015) Long-term EEG in adults: sleep-deprived EEG (SDE), ambulatory EEG (Amb-EEG) and long-term video-EEG recording (LTVER). Clin Neurophysiol. doi: 10.1016/j.neucli.2014.11.004 Google Scholar
  22. Nicolas-Alonso LF, Gomez-Gil J (2012) Brain computer interfaces, a review. Sensors 12(2):1211–1279. doi: 10.3390/s120201211 PubMedCentralPubMedCrossRefGoogle Scholar
  23. Odom JV, Bach M, Brigell M, Holder GE, McCulloch DL, Tormene AP, Vaegan (2010) ISCEV standard for clinical visual evoked potentials (2009 update). Doc Ophthalmol 120(1):111–119. doi: 10.1007/s10633-009-9195-4 PubMedCrossRefGoogle Scholar
  24. Pedrosa P, Machado D, Fiedler P, Alves E, Barradas NP, Haueisen J, Vaz F, Fonseca C (2015a) Electrochemical and structural characterization of nanocomposite Ag y:TiN x thin films for dry bioelectrodes: the effect of the N/Ti ratio and Ag content. Electrochim Acta 153:602–611. doi: 10.1016/j.electacta.2014.12.020 CrossRefGoogle Scholar
  25. Pedrosa P, Fiedler P, Lopes C, Alves E, Barradas NP, Haueisen J, Vaz F, Fonseca C (2015b) Ag:TiN-coated polyurethane for dry biopotential electrodes: from polymer plasma activation to the first EEG measurements. Plasma Process Polym.Google Scholar
  26. Searle A, Kirkup L (2000) A direct comparison of wet, dry and insulating bioelectric recording electrodes. Physiol Meas 21(2):271–283PubMedCrossRefGoogle Scholar
  27. Steinisch M, Tana MG, Comani S (2013) A post-stroke rehabilitation system integrating robotics, VR and high-resolution EEG imaging. IEEE Trans Neural Syst Rehabil Eng 21(5):849–859. doi: 10.1109/TNSRE.2013.2267851 PubMedCrossRefGoogle Scholar
  28. Teplan M (2002) Fundamentals of EEG measurement. Meas Sci Technol 2(2):1–11Google Scholar
  29. Thompson T, Steffert T, Ros T, Leach J, Gruzelier J (2008) EEG applications for sport and performance. Methods 45(4):279–288. doi: 10.1016/j.ymeth.2008.07.006 PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Institute of Biomedical Engineering and InformaticsTechnische Universität IlmenauIlmenauGermany
  2. 2.Departamento de Engenharia Metalúrgica e de Materiais, Faculdade de EngenhariaUniversidade do PortoPortoPortugal
  3. 3.Department of Mechanism TechnologyTechnische Universität IlmenauIlmenauGermany
  4. 4.Centro de FísicaUniversidade do MinhoBragaPortugal
  5. 5.IJN-UTM Cardiovascular Engineering CentreUniversiti Teknologi MalaysiaJohor BahruMalaysia
  6. 6.eemagine Medical Imaging Solutions GmbHBerlinGermany
  7. 7.Department of Neurology, Biomagnetic CenterJena University HospitalJenaGermany

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