Earable Design Analysis for Sleep EEG Measurements

Mandekar, Swati; Jentsch, Lina; Lutz, Kai; Behbahani, Mehdi; Melnykowycz, Mark

doi:10.1145/3460418.3479328

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…Therefore, the placement does not have a significant impact on the signal pickup. In earlier findings [39], for 10 individuals, position E had the lowest impedance, but when the number of subjects is decreased to 8, the location with the lowest impedance value changes, showing once again the significant inter subject variability. The phase plot highlights the capacitive effect of the total impedance which also significantly changes between individuals.…”

Section: Electrode Skin Contact Impedancementioning

confidence: 68%

Advancing towards Ubiquitous EEG, Correlation of In-Ear EEG with Forehead EEG

Mandekar

Holland²,

Thielen³

et al. 2022

Sensors

Self Cite

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Wearable EEG has gained popularity in recent years driven by promising uses outside of clinics and research. The ubiquitous application of continuous EEG requires unobtrusive form-factors that are easily acceptable by the end-users. In this progression, wearable EEG systems have been moving from full scalp to forehead and recently to the ear. The aim of this study is to demonstrate that emerging ear-EEG provides similar impedance and signal properties as established forehead EEG. EEG data using eyes-open and closed alpha paradigm were acquired from ten healthy subjects using generic earpieces fitted with three custom-made electrodes and a forehead electrode (at Fpx) after impedance analysis. Inter-subject variability in in-ear electrode impedance ranged from 20 kΩ to 25 kΩ at 10 Hz. Signal quality was comparable with an SNR of 6 for in-ear and 8 for forehead electrodes. Alpha attenuation was significant during the eyes-open condition in all in-ear electrodes, and it followed the structure of power spectral density plots of forehead electrodes, with the Pearson correlation coefficient of 0.92 between in-ear locations ELE (Left Ear Superior) and ERE (Right Ear Superior) and forehead locations, Fp1 and Fp2, respectively. The results indicate that in-ear EEG is an unobtrusive alternative in terms of impedance, signal properties and information content to established forehead EEG.

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Section: Electrode Skin Contact Impedancementioning

confidence: 68%

Advancing towards Ubiquitous EEG, Correlation of In-Ear EEG with Forehead EEG

Mandekar

Holland²,

Thielen³

et al. 2022

Sensors

Self Cite

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show abstract

“…(2) EEG: has been employed in numerous studies to monitor different human states such as brain diagnostics [58], sleep [67], emotion [57], attention [68], and learning state [37], [16]. Babini et al [16], using an EEG device, designed an experiment to analyze the effect of virtual reality (VR) conditions on students' learning ability and physiological state.…”

Section: B Learning Quantificationmentioning

confidence: 99%

ERUDITE: Human-in-the-Loop IoT for an Adaptive Personalized Learning System

Taherisadr¹,

Faruque²,

Elmalaki³

2023

Preprint

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Thanks to the rapid growth in wearable technologies and recent advancement in machine learning and signal processing, monitoring complex human contexts becomes feasible, paving the way to develop human-in-the-loop IoT systems that naturally evolve to adapt to the human and environment state autonomously. Nevertheless, a central challenge in designing many of these IoT systems arises from the requirement to infer the human mental state, such as intention, stress, cognition load, or learning ability. While different human contexts can be inferred from the fusion of different sensor modalities that can correlate to a particular mental state, the human brain provides a richer sensor modality that gives us more insights into the required human context. This paper proposes ERUDITE, a human-in-the-loop IoT system for the learning environment that exploits recent wearable neurotechnology to decode brain signals.Through insights from concept learning theory, ERUDITE can infer the human state of learning and understand when human learning increases or declines. By quantifying human learning as an input sensory signal, ERUDITE can provide adequate personalized feedback to humans in a learning environment to enhance their learning experience. ERUDITE is evaluated across 15 participants and showed that by using the brain signals as a sensor modality to infer the human learning state and providing personalized adaptation to the learning environment, the participants' learning performance increased on average by 26%. Furthermore, we showed that ERUDITE can be deployed on an edge-based prototype to evaluate its practicality and scalability.

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“…They also have a PPG sensor for cardiac and respiration rate tracking and other sensors. Among the head-worn wearables, in-ear sensors have also gained much interest lately [27,81,89]. For example, Nakamura et al [88] leverage in-ear EEG lexible sensor for overnight sleep stage monitoring.…”

Section: State-of-the-art In Sleep Trackingmentioning

confidence: 99%

PhyMask: Robust Sensing of Brain Activity and Physiological Signals During Sleep with an All-textile Eye Mask

Rostaminia

Homayounfar

Kiaghadi

et al. 2022

ACM Trans. Comput. Healthcare

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Clinical-grade wearable sleep monitoring is a challenging problem since it requires concurrently monitoring brain activity, eye movement, muscle activity, cardio-respiratory features, and gross body movements. This requires multiple sensors to be worn at different locations as well as uncomfortable adhesives and discrete electronic components to be placed on the head. As a result, existing wearables either compromise comfort or compromise accuracy in tracking sleep variables. We propose PhyMask, an all-textile sleep monitoring solution that is practical and comfortable for continuous use and that acquires all signals of interest to sleep solely using comfortable textile sensors placed on the head. We show that PhyMask can be used to accurately measure all the signals required for precise sleep stage tracking and to extract advanced sleep markers such as spindles and K-complexes robustly in the real-world setting. We validate PhyMask against polysomnography and show that it significantly outperforms two commercially-available sleep tracking wearables – Fitbit and Oura Ring.

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Earable Design Analysis for Sleep EEG Measurements

Cited by 7 publications

References 17 publications

Advancing towards Ubiquitous EEG, Correlation of In-Ear EEG with Forehead EEG

Advancing towards Ubiquitous EEG, Correlation of In-Ear EEG with Forehead EEG

ERUDITE: Human-in-the-Loop IoT for an Adaptive Personalized Learning System

PhyMask: Robust Sensing of Brain Activity and Physiological Signals During Sleep with an All-textile Eye Mask

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