Abstract
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.
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Berger, H.: uber das Elektroenkephalogramm des Menschen. Eur. Arch. Psychiatry Clin. Neurosci. 87, 527–570
Guger, C., et al.: Comparison of dry and gel based electrodes for P300 brain-computer interfaces. Front. Neurosci., doi:10.3389/fnins.2012.00060
Wang, L., et al.: PDMS-Based Low Cost Flexible Dry Electrode for Long-Term EEG Measurement. IEEE Sensors Journal 12(9) (September 2012)
Slater, J., et al.: Quality Assessment of Electroencephalography Obtained From a “Dry Electrode” system. Journal of Neuroscience Methods 208, 134–137 (2012)
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)
Dias, N.S., et al.: Wireless Instrumentation System Based on Dry Electrodes for Acquiring EEG Signals. Medical Engineering & Physics 34, 972–981 (2012)
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-2012-4056
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)
Stevens, R., et al.: Modeling the Neurodynamic Complexity of Submarine Navigation Teams. Computational and Mathematical Organization Theory (2012)
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), 87–104
Chung, J.W., et al.: Treatment Outcomes of Mandibular Advancement Device for Obstructive Sleep Apnea Syndrome. Chest 140, 1511–1516
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, 2166–2175
Behneman, A., et al.: Neurotchnology to Accelerate Learning. NEST (2012) (in Press)
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Davis, G., McConnell, C., Popovic, D., Berka, C., Korszen, S. (2013). Soft, Embeddable, Dry EEG Sensors for Real World Applications. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Foundations of Augmented Cognition. AC 2013. Lecture Notes in Computer Science(), vol 8027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39454-6_28
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DOI: https://doi.org/10.1007/978-3-642-39454-6_28
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