Sensing with Earables

Röddiger, Tobias; Clarke, Christopher; Breitling, Paula; Schneegans, Tim; Zhao, Haibin; Gellersen, Hans; Beigl, Michael

doi:10.1145/3550314

Cited by 56 publications

(12 citation statements)

References 235 publications

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“…We can expect an increase in new applications, form factors, and sensor technology as the field grows. Currently, there is scientific evidence to support the use of ear-EEG [ 20 , 21 ] and we set out to provide a set of tools that could benefit further testing and characterization of ear-EEG devices. Our approach was to develop a validation toolkit that would enable the characterization of ear-EEG devices from hardware to neural signal acquisition.…”

Section: Discussionmentioning

confidence: 99%

“…The feasibility of measuring brain signals through ear-EEG devices has been validated in recent publications [ 4 , 18 , 19 ]. For an in-depth review of the field and overview of the technological state-of-the-art around ear-EEG we please refer to [ 20 , 21 ]. A commonality across the ear-EEG literature is that the validation paradigms utilized tend to be known ERP paradigms, namely, the alpha modulation (or alpha blocking) paradigm, the auditory steady-state response (ASSR), the steady-state visual evoked response (SSVEP), auditory evoked potentials (AEPs), visual evoked potentials (VEPs), and oddBall-type paradigms to elicit responses linked to higher processes of the brain, like the P300 and mismatch negativity (MMN) responses.…”

Section: Introductionmentioning

confidence: 99%

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Brain Wearables: Validation Toolkit for Ear-Level EEG Sensors

Correia,

Crosse,

Lopez Valdes

2024

Sensors

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EEG-enabled earbuds represent a promising frontier in brain activity monitoring beyond traditional laboratory testing. Their discrete form factor and proximity to the brain make them the ideal candidate for the first generation of discrete non-invasive brain–computer interfaces (BCIs). However, this new technology will require comprehensive characterization before we see widespread consumer and health-related usage. To address this need, we developed a validation toolkit that aims to facilitate and expand the assessment of ear-EEG devices. The first component of this toolkit is a desktop application (“EaR-P Lab”) that controls several EEG validation paradigms. This application uses the Lab Streaming Layer (LSL) protocol, making it compatible with most current EEG systems. The second element of the toolkit introduces an adaptation of the phantom evaluation concept to the domain of ear-EEGs. Specifically, it utilizes 3D scans of the test subjects’ ears to simulate typical EEG activity around and inside the ear, allowing for controlled assessment of different ear-EEG form factors and sensor configurations. Each of the EEG paradigms were validated using wet-electrode ear-EEG recordings and benchmarked against scalp-EEG measurements. The ear-EEG phantom was successful in acquiring performance metrics for hardware characterization, revealing differences in performance based on electrode location. This information was leveraged to optimize the electrode reference configuration, resulting in increased auditory steady-state response (ASSR) power. Through this work, an ear-EEG evaluation toolkit is made available with the intention to facilitate the systematic assessment of novel ear-EEG devices from hardware to neural signal acquisition.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Brain Wearables: Validation Toolkit for Ear-Level EEG Sensors

Correia,

Crosse,

Lopez Valdes

2024

Sensors

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“…Additional evidence suggests that electrodes placed at the opening of the ear canal might be less prone to signal attenuation for neural sources in the temporal region than those distributed about the scalp (Yarici et al 2023 ). Recent reviews have enumerated the myriad of physiological signals and health phenomena that can be captured using sensors in and around the outer ear (Ne et al 2021 ; Röddiger et al 2022 ; Bleichner and Debener 2017 ). For instance, prior research has already demonstrated the feasibility of using the ear as a recording site not only for electrographic seizures (Zibrandtsen et al 2017 ; Zibrandtsen et al 2018 ), but also for other spontaneous, evoked, or induced neurological activity, such as brain waves associated with sleep (Mikkelsen et al 2019 ), posterior dominant alpha rhythms during eyelid closure (Mikkelsen et al 2015 ; Kappel et al 2019 ; Kaveh et al 2020 ), steady-state visual evoked potentials (Kwak and Lee 2020 ) and auditory brain responses (Christensen et al 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Using a standalone ear-EEG device for focal-onset seizure detection

Joyner,

Hsu,

Martin

et al. 2024

Bioelectron Med

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Background Seizure detection is challenging outside the clinical environment due to the lack of comfortable, reliable, and practical long-term neurophysiological monitoring devices. We developed a novel, discreet, unobstructive in-ear sensing system that enables long-term electroencephalography (EEG) recording. This is the first study we are aware of that systematically compares the seizure detection utility of in-ear EEG with that of simultaneously recorded intracranial EEG. In addition, we present a similar comparison between simultaneously recorded in-ear EEG and scalp EEG. Methods In this foundational research, we conducted a clinical feasibility study and validated the ability of the ear-EEG system to capture focal-onset seizures against 1255 hrs of simultaneous ear-EEG data along with scalp or intracranial EEG in 20 patients with refractory focal epilepsy (11 with scalp EEG, 8 with intracranial EEG, and 1 with both). Results In a blinded, independent review of the ear-EEG signals, two epileptologists were able to detect 86.4% of the seizures that were subsequently identified using the clinical gold standard EEG modalities, with a false detection rate of 0.1 per day, well below what has been reported for ambulatory monitoring. The few seizures not detected on the ear-EEG signals emanated from deep within the mesial temporal lobe or extra-temporally and remained very focal, without significant propagation. Following multiple sessions of recording for a median continuous wear time of 13 hrs, patients reported a high degree of tolerance for the device, with only minor adverse events reported by the scalp EEG cohort. Conclusions These preliminary results demonstrate the potential of using ear-EEG to enable routine collection of complementary, prolonged, and remote neurophysiological evidence, which may permit real-time detection of paroxysmal events such as seizures and epileptiform discharges. This study suggests that the ear-EEG device may assist clinicians in making an epilepsy diagnosis, assessing treatment efficacy, and optimizing medication titration.

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“…The development of reliable and comfortable in-ear electrode systems could, therefore, have significant implications for both mHealth and HMI applications. The widespread and ever-increasing use of headphones, hearing aids, and other audio devices makes the ear space a suitable one for the acquisition of physiological signals in real time or as an HMI [ 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Channel Soft Dry Electrodes for Electrocardiography Acquisition in the Ear Region

van der Heijden,

Gilbert,

Jafari

et al. 2024

Sensors

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In-ear acquisition of physiological signals, such as electromyography (EMG), electrooculography (EOG), electroencephalography (EEG), and electrocardiography (ECG), is a promising approach to mobile health (mHealth) due to its non-invasive and user-friendly nature. By providing a convenient and comfortable means of physiological signal monitoring, in-ear signal acquisition could potentially increase patient compliance and engagement with mHealth applications. The development of reliable and comfortable soft dry in-ear electrode systems could, therefore, have significant implications for both mHealth and human–machine interface (HMI) applications. This research evaluates the quality of the ECG signal obtained with soft dry electrodes inserted in the ear canal. An earplug with six soft dry electrodes distributed around its perimeter was designed for this study, allowing for the analysis of the signal coming from each electrode independently with respect to a common reference placed at different positions on the body of the participants. An analysis of the signals in comparison with a reference signal measured on the upper right chest (RA) and lower left chest (LL) was performed. The results show three typical behaviors for the in-ear electrodes. Some electrodes have a high correlation with the reference signal directly after inserting the earplug, other electrodes need a settling time of typically 1–3 min, and finally, others never have a high correlation. The SoftPulseTM electrodes used in this research have been proven to be perfectly capable of measuring physiological signals, paving the way for their use in mHealth or HMI applications. The use of multiple electrodes distributed in the ear canal has the advantage of allowing a more reliable acquisition by intelligently selecting the signal acquisition locations or allowing a better spatial resolution for certain applications by processing these signals independently.

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Sensing with Earables

Cited by 56 publications

References 235 publications

Brain Wearables: Validation Toolkit for Ear-Level EEG Sensors

Brain Wearables: Validation Toolkit for Ear-Level EEG Sensors

Using a standalone ear-EEG device for focal-onset seizure detection

Multi-Channel Soft Dry Electrodes for Electrocardiography Acquisition in the Ear Region

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