Custom-Fitted In- and Around-the-Ear Sensors for Unobtrusive and On-the-Go EEG Acquisitions: Development and Validation

Valentin, Olivier; Viallet, Guilhem; Delnavaz, Aidin; Crétot-Richert, Gabrielle; Ducharme, Mikaël; Monsarat-Chanon, Hami; Voix, Jérémie

doi:10.3390/s21092953

Cited by 16 publications

(19 citation statements)

References 48 publications

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“…The values for wet measurements for all electrodes align with the wet electrode impedance values found in the literature, 34 kΩ (SD = 37 kΩ) [9]. The irregular shape of the ear canal causes different contact pressure on the foam bud and based on anatomy, will likely be the highest at the ELE position [20]. This is also reflected in the impedance results for both sized and contact conditions.…”

Section: Electrode Locationsupporting

confidence: 84%

Earable Design Analysis for Sleep EEG Measurements

Mandekar

Jentsch

Lutz

et al. 2021

Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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Conventional EEG devices cannot be used in everyday life and hence, past decade research has been focused on Ear-EEG for mobile, at-home monitoring for various applications ranging from emotion detection to sleep monitoring. As the area available for electrode contact in the ear is limited, the electrode size and location play a vital role for an Ear-EEG system. In this investigation, we present a quantitative study of ear-electrodes with two electrode sizes at different locations in a wet and dry configuration. Electrode impedance scales inversely with size and ranges from 450 kΩ to 1.29 MΩ for dry and from 22 kΩ to 42 kΩ for wet contact at 10 Hz. For any size, the location in the ear canal with the lowest impedance is ELE (Left Ear Superior), presumably due to increased contact pressure caused by the outer-ear anatomy. The results can be used to optimize signal pickup and SNR for specific applications. We demonstrate this by recording sleep spindles during sleep onset with high quality (5.27 µVrms). CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing; Ubiquitous and mobile computing systems and tools.

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Section: Electrode Locationsupporting

confidence: 84%

Earable Design Analysis for Sleep EEG Measurements

Mandekar

Jentsch

Lutz

et al. 2021

Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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“…We are now able to move measures of EEG outside of the lab and record frequently across hours or days, this information can be stored and or relayed back to the researchers. A key feature of wearable EEG is the integration of the electrodes and connecting leads into headsets or ear-level devices ( 102 , 104 ). Various forms of wearable EEG have been developed and trialed, silver electrodes in a custom earmold ( 108 ) cloth electrodes with non-custom earbud ( 96 , 109 ) conductive silicone electrodes in the ear and around the ear ( 104 ) and fingered electrodes to improve contact ( 97 ).…”

Section: Resultsmentioning

confidence: 99%

“…A key feature of wearable EEG is the integration of the electrodes and connecting leads into headsets or ear-level devices ( 102 , 104 ). Various forms of wearable EEG have been developed and trialed, silver electrodes in a custom earmold ( 108 ) cloth electrodes with non-custom earbud ( 96 , 109 ) conductive silicone electrodes in the ear and around the ear ( 104 ) and fingered electrodes to improve contact ( 97 ). At present there is a tradeoff between the convenience and usability of these devices and resolution and/or range of applications.…”

Section: Resultsmentioning

confidence: 99%

“…Although there is no single physiological objective measure of tinnitus itself, years of lab based search has identified many related markers of tinnitus related activity, for example neural networks associated with tinnitus (93), measures of emotion, and stress (94). New miniaturized wearable technology is now available to make longitudinal measures of physiological function (95)(96)(97)(98)(99)(100)(101)(102)(103)(104) that can be related to behavioral indices of tinnitus.…”

Section: Biosensorsmentioning

confidence: 99%

See 1 more Smart Citation

A State-of-Art Review of Digital Technologies for the Next Generation of Tinnitus Therapeutics

Searchfield

Sanders

Doborjeh

et al. 2021

Front. Digit. Health

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Background: Digital processing has enabled the development of several generations of technology for tinnitus therapy. The first digital generation was comprised of digital Hearing Aids (HAs) and personal digital music players implementing already established sound-based therapies, as well as text based information on the internet. In the second generation Smart-phone applications (apps) alone or in conjunction with HAs resulted in more therapy options for users to select from. The 3rd generation of digital tinnitus technologies began with the emergence of many novel, largely neurophysiologically-inspired, treatment theories that drove development of processing; enabled through HAs, apps, the internet and stand-alone devices. We are now of the cusp of a 4th generation that will incorporate physiological sensors, multiple transducers and AI to personalize therapies.Aim: To review technologies that will enable the next generations of digital therapies for tinnitus.Methods: A “state-of-the-art” review was undertaken to answer the question: what digital technology could be applied to tinnitus therapy in the next 10 years? Google Scholar and PubMed were searched for the 10-year period 2011–2021. The search strategy used the following key words: “tinnitus” and [“HA,” “personalized therapy,” “AI” (and “methods” or “applications”), “Virtual reality,” “Games,” “Sensors” and “Transducers”], and “Hearables.” Snowballing was used to expand the search from the identified papers. The results of the review were cataloged and organized into themes.Results: This paper identified digital technologies and research on the development of smart therapies for tinnitus. AI methods that could have tinnitus applications are identified and discussed. The potential of personalized treatments and the benefits of being able to gather data in ecologically valid settings are outlined.Conclusions: There is a huge scope for the application of digital technology to tinnitus therapy, but the uncertain mechanisms underpinning tinnitus present a challenge and many posited therapeutic approaches may not be successful. Personalized AI modeling based on biometric measures obtained through various sensor types, and assessments of individual psychology and lifestyles should result in the development of smart therapy platforms for tinnitus.

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“…It should already have an intelligence sensing mechanism. Meaning that as long as the sensor touched the ear surface, data would still be obtained as the similar mechanism worked in ear-EEG Sensors [32]. This can be compared to the ear-EEG sensor in [19] which is behind the patient's ear.…”

Section: Muse Tm Headbandmentioning

confidence: 99%

Implementation of Closing Eyes Detection with Ear Sensor of Muse EEG Headband using Support Vector Machine Learning

2023

IJIES

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Epilepsy patients might need continuous electroencephalography (EEG) monitoring to help them understand their own condition's improvements or their doctors to determine the frequency of the seizures. In this study, we demonstrated how a low-cost, portable EEG headband could be used to detect absence seizures in epilepsy patients. The method used is Support Vector Machine (SVM) to separate the initial limit and Machine Learning with Tensorflow to predict its confidence level. Then, we tried to test our method on 2 other patients to see its measurement divergence. Furthermore, the Tensorflow library has been applied and developed to train and classify the data, which is also one of the novelty approaches in this study. From the result, closed eyes can be detected from open-eye conditions with more than 96 % accuracy and a loss of only 2.35%. The findings demonstrate the feasibility of detecting absence seizures using only two electrodes which were TP9 and TP10 of Muse headband which is also positioned in the ear like an ear-EEG. Overall, the study successfully developed a novel method utilizing a low-cost headband to provide affordable health system access.

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Custom-Fitted In- and Around-the-Ear Sensors for Unobtrusive and On-the-Go EEG Acquisitions: Development and Validation

Cited by 16 publications

References 48 publications

Earable Design Analysis for Sleep EEG Measurements

Earable Design Analysis for Sleep EEG Measurements

A State-of-Art Review of Digital Technologies for the Next Generation of Tinnitus Therapeutics

Implementation of Closing Eyes Detection with Ear Sensor of Muse EEG Headband using Support Vector Machine Learning

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