Misplaced Pages

Ear-EEG

Article snapshot taken from Wikipedia with creative commons attribution-sharealike license. Give it a read and then ask your questions in the chat. We can research this topic together.
Medical diagnostic method
Ear-EEG
Examples of in-ear EEG mounts. On the left is seen a single earplug (right ear), on the right is seen a right earplug in ear
Purposemeasure dynamics of brain activity

Ear-EEG is a method for measuring dynamics of brain activity through the minute voltage changes observable on the skin, typically by placing electrodes on the scalp. In ear-EEG, the electrodes are exclusively placed in or around the outer ear, resulting in both a much greater invisibility and wearer mobility compared to full scalp electroencephalography (EEG), but also significantly reduced signal amplitude, as well as reduction in the number of brain regions in which activity can be measured. It may broadly be partitioned into two groups: those using electrode positions exclusively within the concha and ear canal, and those also placing electrodes close to the ear, usually hidden behind the ear lobe. Generally speaking, the first type will be the most invisible, but also offer the most challenging (noisy) signal. Ear-EEG is a good candidate for inclusion in a hearable device, however, due to the high complexity of ear-EEG sensors, this has not yet been done.

History

Ear-EEG was first described in a patent application, and subsequently in other publications. Since then, it has grown to be an endeavor spread across multiple research groups and collaborations, as well as private companies. Notable incarnations of the technology are the cEEGrid (see picture to the right) and the custom 3D-printed ear plugs from NeuroTechnology Lab (see picture above). Attempts at creating in-ear generic earpieces are also known to be under way.

Demonstration of multiple cEEGrids on dummy heads

Uses in research

It is possible to think of multiple research areas in which an unobtrusive and invisible EEG system would be beneficial. Good examples are in studies of group dynamics or didactics, in which cases it would be very valuable to be able to monitor the effect of various events on individuals, while still letting them experience said events unfettered. And in this context, it is very important to perform detailed comparisons between ear-EEG and regular scalp EEG, as results need to be comparable across platforms. This has been done in multiple papers. In these it has been found that ear-EEG measurements are comparable to scalp EEG in the frequency domain; however, the time domain activity recorded by the two systems are notably different. Several papers have presented models (i.e. ear-EEG forward models) of how the electric field from electrical sources in the brain maps to potentials in the ear. The ear-EEG forward models enable prediction of the potentials in the ear for a specific neural phenomenon, and can be used to improve the understanding of which neural sources that can be measured with ear-EEG

Example of a scalp topography (middle) with corresponding ear-topographies (left and right). The topographies show the potential on the scalp and in the ears for a single dipolar brain source and were calculated using an individualized ear-EEG forward model as described by Kappel et al.

Dry-contact electrode ear-EEG

Dry-contact electrode ear-EEG is a method in which no gel is applied between the electrode and the skin. This method generally improves the comfort and user-friendliness for long-term and real-life recordings. Because no gel is applied to the electrodes, the user can potentially mount the ear-EEG device without assistance.

High-density ear-EEG.
Example of high-density ear-EEG. On the left is seen a high-density ear-EEG earpiece mounted in the ear. On the right is a picture of a high-density ear-EEG soft-earpiece with dry-contact electrodes.

Dry-contact electrode ear-EEG have been used to perform high-density ear-EEG recordings, which enable mapping of the brain response on a topographic 3D map of the ear (Ear-topographies).

When using dry-contact electrodes, the interface between the skin and the electrodes are mainly defined by the electrochemical properties of the electrode material, the mechanical design of the electrode, the surface properties of the electrode, and how the electrode is retained against the skin. To improve these aspects for ear-EEG, nanostructured electrodes and soft earpieces have been proposed. The electronic instrumentation must also be carefully designed to accommodate dry-contact electrodes.

Real-life monitoring

The state of the human brain is influenced by the surrounding environment, and the response from the brain is influenced by the state of the brain. Thus, restricting brain research to a laboratory represents a fundamental limitation. Real-life monitoring of ear-EEG overcome this limitation, and enable research of evoked responses and spontaneous responses related to everyday life situations.

The compact and discreet nature of ear-EEG devices makes it suitable for real-life EEG monitoring. A general problem when recordings EEG is the interference arising from noise and artifacts. In a laboratory environment, artifacts and interference can largely be avoided or controlled, in real-life this is challenging. Physiological artifacts are a category of artifacts with physiological origin, in contrast to artifacts arising from electrical interference. A study of physiological artifacts in ear-EEG found artifacts from jaw muscle contractions to be higher for ear-EEG compared to the scalp EEG, whereas eye-blinking did not influence the ear-EEG.

Sleep monitoring

A promising use case is in long-term sleep monitoring, where there is presently a need for a more user friendly (and cheaper) alternative to the gold standard polysomnography. Innovation Fund Denmark recently funded a large project on using ear-EEG for sleep monitoring, in a collaboration between industry and Aarhus University in Denmark , however, development of an ear-EEG based sleep monitor is a global endeavor, with other prominent examples taking place at the University of Colorado , Imperial College London as well as the University of Oxford.

Possible commercial uses

Despite the lack of ear-EEG products on the market, several companies have revealed investments in ear-EEG technology. Foremost of these are the hearing aid producers Oticon and Widex and its sister company T&W Engineering, who are looking into hearing-aid applications, the feasibility of which there appears to be some support for, and a hypoglycemia alarm.

Other potential use cases which are known to have been explored are driver drowsiness detection, BCI and biometric identification.

References

  1. US application 2007112277, Fischer, Russell; Ferraro, Joseph & Lal, Prince et al., "Apparatus and method for the measurement and monitoring of bioelectric signal patterns", published 2007-05-17 , since abandoned.
  2. EP 2448477, Kidmose, Preben; Ungstrup, Michael & Rank, Mike L., "An ear plug with surface electrodes", published 2012-05-09, assigned to Widex A/S 
  3. Kidmose, Preben (2012). "Auditory evoked responses from Ear-EEG recordings". 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. EMBC 2012. San Diego, Cal. pp. 586–589. doi:10.1109/EMBC.2012.6345999. ISBN 978-1-4577-1787-1.
  4. Bleichner, Martin (6 April 2015). "Exploring miniaturized EEG electrodes for brain-computer interfaces. An EEG you do not see?". Physiol Rep. 3 (4): e12362. doi:10.14814/phy2.12362. PMC 4425967. PMID 25847919.
  5. "The Aware". United Sciences. 20 April 2016. Retrieved 25 August 2016.
  6. Fiedler, Lorenz. Ear-EEG Allows Extraction of Neural Responses in Challenging Listening Scenarios – A Future Technology for Hearing Aids?. EMBC 2016. Orlando, Fl.
  7. ^ Debener, Stefan (17 November 2015). "Unobtrusive ambulatory EEG using a smartphone and flexible printed electrodes around the ear". Scientific Reports. 5: 16743. Bibcode:2015NatSR...516743D. doi:10.1038/srep16743. PMC 4648079. PMID 26572314.
  8. "cEEGrid –". www.ceegrid.com. Retrieved 2016-11-14.
  9. Kidmose, P.; Looney, D.; Jochumsen, L.; Mandic, D. P. (July 2013). "Ear-EEG from generic earpieces: A feasibility study". 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Vol. 2013. IEEE. pp. 543–546. doi:10.1109/embc.2013.6609557. ISBN 9781457702167. PMID 24109744. S2CID 10278053.
  10. Dong, Hao. A New Soft Material based In-The-Ear EEG Recording Technique. EMBC 2016. Orlando, Fl.
  11. Goverdovsky, Valentin. Generic Viscoelastic In-Ear EEG Monitor. EMBC 2016. Orlando, Fl.
  12. Goverdovsky, Valentin (1 January 2016). "In-Ear EEG From Viscoelastic Generic Earpieces: Robust and Unobtrusive 24/7 Monitoring". IEEE Sensors Journal. 16 (1). IEEE: 271–277. Bibcode:2016ISenJ..16..271G. doi:10.1109/JSEN.2015.2471183. hdl:10044/1/43182. S2CID 44224053.
  13. Norton, James (31 March 2015). "Soft, curved electrode systems capable of integration on the auricle as a persistent brain–computer interface". PNAS. 112 (13): 3920–3925. Bibcode:2015PNAS..112.3920N. doi:10.1073/pnas.1424875112. PMC 4386388. PMID 25775550.
  14. Casson, Alexander (10 May 2010). "Wearable electroencephalography. What is it, why is it needed, and what does it entail?" (PDF). IEEE Engineering in Medicine and Biology Magazine. 29 (3): 44–56. doi:10.1109/MEMB.2010.936545. hdl:10044/1/5910. PMID 20659857. S2CID 1891995.
  15. Mikkelsen, Kaare (18 November 2015). "EEG Recorded from the Ear: Characterizing the Ear-EEG Method". Frontiers in Neuroscience. 9: 438. doi:10.3389/fnins.2015.00438. PMC 4649040. PMID 26635514.
  16. Bleichner, Martin (5 October 2016). "Identifying auditory attention with ear-EEG: cEEGrid versus high-density cap-EEG comparison". Journal of Neural Engineering. 13 (6): 066004. Bibcode:2016JNEng..13f6004B. doi:10.1088/1741-2560/13/6/066004. PMID 27705963.
  17. ^ Dong, Hao (18 October 2016). "A new soft material based in-the-ear EEG recording technique". 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Vol. 2016. pp. 5709–5712. doi:10.1109/EMBC.2016.7592023. hdl:10044/1/44965. ISBN 978-1-4577-0220-4. PMID 28269551. S2CID 27566415.
  18. ^ Kappel, Simon L.; Makeig, Scott; Kidmose, Preben (2019-09-10). "Ear-EEG Forward Models: Improved Head-Models for Ear-EEG". Frontiers in Neuroscience. 13: 943. doi:10.3389/fnins.2019.00943. ISSN 1662-453X. PMC 6747017. PMID 31551697.
  19. Goverdovsky, Valentin; von Rosenberg, Wilhelm; Nakamura, Takashi; Looney, David; Sharp, David J.; Papavassiliou, Christos; Morrell, Mary J.; Mandic, Danilo P. (December 2017). "Hearables: Multimodal physiological in-ear sensing". Scientific Reports. 7 (1): 6948. arXiv:1609.03330. Bibcode:2017NatSR...7.6948G. doi:10.1038/s41598-017-06925-2. ISSN 2045-2322. PMC 5537365. PMID 28761162.
  20. Kidmose, Preben; Looney, David; Ungstrup, Michael; Rank, Mike Lind; Mandic, Danilo P. (October 2013). "A Study of Evoked Potentials From Ear-EEG". IEEE Transactions on Biomedical Engineering. 60 (10): 2824–2830. doi:10.1109/TBME.2013.2264956. ISSN 0018-9294. PMID 23722447. S2CID 12550407.
  21. ^ Hoon Lee, Joong; Min Lee, Seung; Jin Byeon, Hang; Sook Hong, Joung; Suk Park, Kwang; Lee, Sang-Hoon (August 2014). "CNT/PDMS-based canal-typed ear electrodes for inconspicuous EEG recording". Journal of Neural Engineering. 11 (4): 046014. Bibcode:2014JNEng..11d6014L. doi:10.1088/1741-2560/11/4/046014. ISSN 1741-2552. PMID 24963747. S2CID 37513095.
  22. ^ Kappel, Simon L.; Kidmose, Preben (July 2018). "Real-Life Dry-Contact Ear-EEG". 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Vol. 2018. pp. 5470–5474. doi:10.1109/embc.2018.8513532. ISBN 9781538636466. PMID 30441575. S2CID 53093217.
  23. Xiong Zhou; Qiang Li; Kilsgaard, Soren; Moradi, Farshad; Kappel, Simon L.; Kidmose, Preben (June 2016). "A wearable ear-EEG recording system based on dry-contact active electrodes". 2016 IEEE Symposium on VLSI Circuits (VLSI-Circuits). pp. 1–2. doi:10.1109/VLSIC.2016.7573559. ISBN 9781509006359. S2CID 37530730.
  24. ^ Kappel, Simon L.; Kidmose, Preben (July 2017). "High-density ear-EEG". 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Vol. 2017. pp. 2394–2397. doi:10.1109/embc.2017.8037338. ISBN 9781509028092. PMID 29060380. S2CID 8902094.
  25. ^ Kappel, Simon L.; Rank, Mike Lind; Toft, Hans Olaf; Andersen, Mikael; Kidmose, Preben (2018). "Dry-Contact Electrode Ear-EEG". IEEE Transactions on Biomedical Engineering. 66 (1): 150–158. doi:10.1109/tbme.2018.2835778. ISSN 0018-9294. PMID 29993415. S2CID 51614629.
  26. Chi, Yu Mike; Jung, Tzyy-Ping; Cauwenberghs, Gert (2010). "Dry-Contact and Noncontact Biopotential Electrodes: Methodological Review". IEEE Reviews in Biomedical Engineering. 3: 106–119. CiteSeerX 10.1.1.227.2124. doi:10.1109/RBME.2010.2084078. ISSN 1937-3333. PMID 22275204. S2CID 2705602.
  27. Kappel, Simon L.; Kidmose, Preben (August 2015). "Study of impedance spectra for dry and wet EarEEG electrodes". 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Vol. 2015. pp. 3161–3164. doi:10.1109/embc.2015.7319063. ISBN 9781424492718. PMID 26736963. S2CID 450962.
  28. Xiong Zhou; Qiang Li; Kilsgaard, Søren; Moradi, Farshad; Kappel, Simon L.; Kidmose, Preben (June 2016). "A wearable ear-EEG recording system based on dry-contact active electrodes". 2016 IEEE Symposium on VLSI Circuits (VLSI-Circuits). pp. 1–2. doi:10.1109/vlsic.2016.7573559. ISBN 9781509006359. S2CID 37530730.
  29. Bleichner, Martin G.; Debener, Stefan (2017-04-07). "Concealed, Unobtrusive Ear-Centered EEG Acquisition: cEEGrids for Transparent EEG". Frontiers in Human Neuroscience. 11: 163. doi:10.3389/fnhum.2017.00163. ISSN 1662-5161. PMC 5383730. PMID 28439233.
  30. Kappel, Simon L. (September 2016). Development and Characterization of Ear-EEG for Real-Life Brain-Monitoring (Ph.D. thesis). Aarhus University. doi:10.7146/aul.260.183. ISBN 9788775074204.
  31. Bleichner, Martin G.; Lundbeck, Micha; Selisky, Matthias; Minow, Falk; Jäger, Manuela; Emkes, Reiner; Debener, Stefan; De Vos, Maarten (April 2015). "Exploring miniaturized EEG electrodes for brain-computer interfaces. An EEG you do not see?". Physiological Reports. 3 (4): e12362. doi:10.14814/phy2.12362. ISSN 2051-817X. PMC 4425967. PMID 25847919.
  32. Fiedler, L.; Obleser, J.; Lunner, T.; Graversen, C. (August 2016). "Ear-EEG allows extraction of neural responses in challenging listening scenarios — A future technology for hearing aids?". 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Vol. 2016. pp. 5697–5700. doi:10.1109/EMBC.2016.7592020. ISBN 9781457702204. PMID 28269548. S2CID 206635991.
  33. ^ Pham, Nhat; Dinh, Tuan; Raghebi, Zohreh; Kim, Taeho; Bui, Nam; Nguyen, Phuc; Truong, Hoang; Banaei-Kashani, Farnoush; Halbower, Ann; Dinh, Thang; Vu, Tam (2020-06-15). "WAKE". Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services. MobiSys '20. Toronto, Ontario, Canada: Association for Computing Machinery. pp. 404–418. doi:10.1145/3386901.3389032. ISBN 978-1-4503-7954-0. S2CID 219398352.
  34. Kappel, Simon L.; Looney, David; Mandic, Danilo P.; Kidmose, Preben (2017-08-11). "Physiological artifacts in scalp EEG and ear-EEG". BioMedical Engineering OnLine. 16 (1): 103. doi:10.1186/s12938-017-0391-2. ISSN 1475-925X. PMC 5553928. PMID 28800744.
  35. Kappel, Simon L.; Looney, David; Mandic, Danilo P.; Kidmose, Preben (August 2014). "A method for quantitative assessment of artifacts in EEG, and an empirical study of artifacts". 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol. 2014. pp. 1686–1690. doi:10.1109/embc.2014.6943931. ISBN 9781424479290. PMID 25570299. S2CID 12524339.
  36. Looney, David; Goverdovsky, Valentin; Rosenzweig, Ivana; Morrell, Mary J.; Mandic, Danilo P. (December 2016). "Wearable In-Ear Encephalography Sensor for Monitoring Sleep. Preliminary Observations from Nap Studies". Annals of the American Thoracic Society. 13 (12): 2229–2233. doi:10.1513/AnnalsATS.201605-342BC. ISSN 2329-6933. PMC 5291497. PMID 27684316.
  37. Stochholm, Andreas. Automatic Sleep Stage Classification using Ear-EEG. EMBC 2016. Orlando, Fl.
  38. Mikkelsen, Kaare (2017). "Automatic sleep staging using ear-EEG". BioMedical Engineering OnLine. 16 (1): 111. doi:10.1186/s12938-017-0400-5. PMC 5606130. PMID 28927417.
  39. Nguyen, Anh; Alqurashi, Raghda; Raghebi, Zohreh; Banaei-kashani, Farnoush; Halbower, Ann C.; Vu, Tam (2016). "A Lightweight and Inexpensive In-ear Sensing System for Automatic Whole-night Sleep Stage Monitoring". Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM. SenSys '16. Stanford, CA, USA: ACM Press. pp. 230–244. doi:10.1145/2994551.2994562. ISBN 9781450342636. S2CID 11709648.
  40. "Øreprop skal aflæse søvnløses hjerneaktivitet". Innovation Fund Denmark. Retrieved 4 January 2018.
  41. Nguyen, Anh. A Lightweight and Inexpensive In-ear Sensing System For Automatic Whole-night Sleep Stage Monitoring. 14th ACM Conference on Embedded Network Sensor System. Stanford, USA.
  42. Moss, James (2017). "The Efficacy of In-Ear Electroencephalography (EEG) to Monitor Sleep Latency and the Impact of Sleep Deprivation". A80-C. NOVEL DIAGNOSTIC APPROACHES TO SDB. American Thoracic Society International Conference Abstracts. American Thoracic Society. pp. A7596. doi:10.1164/ajrccm-conference.2017.195.1_MeetingAbstracts.A7596 (inactive 1 November 2024).{{cite book}}: CS1 maint: DOI inactive as of November 2024 (link)
  43. Fiedler, Lorenz. Around-the-Ear EEG Reflects the Attended Speaker in Multi-Speaker Scenario. EMBC 2016. Orlando, Fl.
  44. "Ear EEG-based Hypoglycaemia Alarm". Aarhus University. Retrieved 31 August 2016.
  45. Mirkovic, Bojana (27 July 2016). "Target Speaker Detection with Concealed EEG Around the Ear". Frontiers in Neuroscience. 9: 438. doi:10.3389/fnins.2015.00438. PMC 4649040. PMID 26635514.
  46. Mirkovic, Bojana. Ear-EEG Allows Extraction of Neural Responses in Challenging Listening Scenarios – A Future Technology for Hearing Aids?. EMBC 2016. Orlando, Fl.
  47. Hwang, Taeho. Driver Drowsiness Detection using the In-Ear EEG. EMBC 2016. Orlando, Fl.
  48. Choi, Soo-In. Feasibility of using Ear EEG for Developing a Practical Brain-Computer Interface System: A Preliminary Study. EMBC 2016. Orlando, Fl.
  49. Yu-Te Wang; Nakanishi, Masaki; Kappel, Simon Lind; Kidmose, Preben; Mandic, Danilo P.; Yijun Wang; Chung-Kuan Cheng; Tzyy-Ping Jung (August 2015). "Developing an online steady-state visual evoked potential-based brain-computer interface system using EarEEG". 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Vol. 2015. pp. 2271–2274. doi:10.1109/embc.2015.7318845. ISBN 9781424492718. PMID 26736745. S2CID 5996098.
  50. Yang, Jong-Kai. Passthoughts Authentication with Low Cost EarEEG. EMBC 2016. Orlando, Fl.
Categories: