Ear-EEG
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.
Ear-EEG | |
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Purpose | measure dynamics of brain activity |
History
Ear-EEG was first described in "A1 US patent US20070112277 A1",[1] though other noteworthy mentions are "B1 EP patent EP2448477 B1"[2] and "Auditory evoked responses from Ear-EEG recordings".[3] Since then, it has grown to be an endeavor spread across multiple research groups[4] and collaborations, as well as private companies.[5][6] Notable incarnations of the technology are the cEEGrid [7][8] (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.[9][10][11][12][13]
Uses in research
It is possible to think of multiple research areas in which an unobtrusive and invisible EEG system would be beneficial.[14] 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.[7][15][16][17] 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.[18][19][20] 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[18]
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.[21][22][23] 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.
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).[24]
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.[26] To improve these aspects for ear-EEG, nanostructured electrodes and soft earpieces have been proposed.[25] The electronic instrumentation must also be carefully designed to accommodate dry-contact electrodes.[27][28]
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.[29][22]
The compact and discreet nature of ear-EEG devices makes it suitable for real-life EEG monitoring.[30][31][21][32][33] 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.[34][35]
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.[36][37][38][39] 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 ,[40] however, development of an ear-EEG based sleep monitor is a global endeavor, with other prominent examples taking place at the University of Colorado ,[41] Imperial College London [42][17] as well as the University of Oxford.[33]
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 [43] and Widex,[44] who are looking into hearing-aid applications, the feasibility of which there appears to be some support for,[45][46] and a hypoglycemia alarm.
Other potential use cases which are known to have been explored are driver drowsiness detection,[47] BCI[48][49] and biometric identification.[50]
References
- A1 US patent US20070112277 A1
- B1 EP patent EP2448477 B1
- 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.
- 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.
- "The Aware". United Sciences. 20 April 2016. Retrieved 25 August 2016.
- Fiedler, Lorenz. Ear-EEG Allows Extraction of Neural Responses in Challenging Listening Scenarios – A Future Technology for Hearing Aids?. EMBC 2016. Orlando, Fl.
- 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.
- "cEEGrid –". www.ceegrid.com. Retrieved 2016-11-14.
- 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). IEEE. 2013: 543–546. doi:10.1109/embc.2013.6609557. ISBN 9781457702167. PMID 24109744. S2CID 10278053.
- Dong, Hao. A New Soft Material based In-The-Ear EEG Recording Technique. EMBC 2016. Orlando, Fl.
- Goverdovsky, Valentin. Generic Viscoelastic In-Ear EEG Monitor. EMBC 2016. Orlando, Fl.
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- 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.
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- 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.
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- 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.
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- 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.
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