Soft, Embeddable, Dry EEG Sensors for Real World Applications

  • Gene Davis
  • Catherine McConnell
  • Djordje Popovic
  • Chris Berka
  • Stephanie Korszen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8027)


Over the last decade, numerous papers have presented the use of dry electrodes capable of acquiring electroencephalogram (EEG) signals through hair. A few of these dry electrode prototypes have even progressed from lab-based EEG acquisition to commercial sales. While the field has improved rapidly as of late, most dry electrodes share a number of shortcomings that limit their potential real world applications including: 1) multiple rigid prongs that require sustained pressure to penetrate hair and maintain solid scalp contact, creating higher levels of discomfort when compared to standard wet sensors; 2) cumbersome or chin-strap-type applications for maintaining electrode contact, creating barriers to end user acceptance; 3) rigid active electrodes to compensate for high input impedances that limit flexibility and placement of sensors; 4) inability to safely imbed sensors under protective headgear, restricting use in some fields where EEG metrics are most desired; and 5) expensive sensor manufacturing that drives costs high for use across subjects. Under a recent DARPA Phase 3 contract, Advanced Brain Monitoring has developed a novel semi-dry sensor that addresses the current dry electrode shortcomings, opening up the door for new real world applications without compromising subject safety or comfort. The semi-dry sensor prototype was tested during a live performance requirement at the end of Phase 3, and successfully acquired EEG across all subject hair types over a 3 day testing period. The results from the performance requirement and subsequent results for new advancements to the prototype are presented here.


Electroencephalograms (EEG) dry-electrodes wearable EEG BCI Real World Applications 


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  1. 1.
    Berger, H.: uber das Elektroenkephalogramm des Menschen. Eur. Arch. Psychiatry Clin. Neurosci. 87, 527–570Google Scholar
  2. 2.
    Guger, C., et al.: Comparison of dry and gel based electrodes for P300 brain-computer interfaces. Front. Neurosci., doi:10.3389/fnins.2012.00060Google Scholar
  3. 3.
    Wang, L., et al.: PDMS-Based Low Cost Flexible Dry Electrode for Long-Term EEG Measurement. IEEE Sensors Journal 12(9) (September 2012)Google Scholar
  4. 4.
    Slater, J., et al.: Quality Assessment of Electroencephalography Obtained From a “Dry Electrode” system. Journal of Neuroscience Methods 208, 134–137 (2012)CrossRefGoogle Scholar
  5. 5.
    Forvi, E., et al.: Preliminary Technological Assessment of Microneedles-Base Dry Electrodes for Biopotential Monitoring In Clinical Examinations. Sensors and Actuators A 180, 177–186 (2012)CrossRefGoogle Scholar
  6. 6.
    Dias, N.S., et al.: Wireless Instrumentation System Based on Dry Electrodes for Acquiring EEG Signals. Medical Engineering & Physics 34, 972–981 (2012)CrossRefGoogle Scholar
  7. 7.
    Ghoshdastider, U., et al.: Development of a Wearable and Wireless, Modular, Multichannel, EEG-System Utilising Dry-Electrodes for Long Time Monitoring. Biomed Tech. (2012), doi:10.1515/bmt-2012-4056Google Scholar
  8. 8.
    Berka, C., et al.: Real-Time Analysis of EEG Indexes of Alertness, Cognition, and Memory Acquired With a Wireless EEG Headset. International Journal of Human-Computer Interaction 17(2), 151–170 (2004)CrossRefGoogle Scholar
  9. 9.
    Stevens, R., et al.: Modeling the Neurodynamic Complexity of Submarine Navigation Teams. Computational and Mathematical Organization Theory (2012)Google Scholar
  10. 10.
    Berka, C., et al.: Accelerating Training Using Interactive Neuro-Educational Technologies: Applications to Archery, Golf, and Rifle Marksmanship. International Journal of Sports and Society 1(4), 87–104Google Scholar
  11. 11.
    Chung, J.W., et al.: Treatment Outcomes of Mandibular Advancement Device for Obstructive Sleep Apnea Syndrome. Chest 140, 1511–1516Google Scholar
  12. 12.
    Westbrook, P., et al.: Description and Validation of the Apnea Risk Evaluation System: a Novel Method to Diagnose Sleep Apnea-Hypopnea in the Home. Chest 128, 2166–2175Google Scholar
  13. 13.
    Behneman, A., et al.: Neurotchnology to Accelerate Learning. NEST (2012) (in Press)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gene Davis
    • 1
  • Catherine McConnell
    • 1
  • Djordje Popovic
    • 1
  • Chris Berka
    • 1
  • Stephanie Korszen
    • 1
  1. 1.Advanced Brain Monitoring, Inc.CarlsbadUSA

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