Development and Characterization of Ear-EEG for Real-Life Brain-Monitoring

Kappel, Simon L.

doi:10.7146/aul.260.183

Cited by 6 publications

(4 citation statements)

References 68 publications

(115 reference statements)

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“…Ear-EEG is a method in which EEG is measured from electrodes placed in the ear (Kidmose et al, 2013; Bleichner et al, 2015; Mikkelsen et al, 2015). The main advantage of ear-EEG is that it enables discreet and unobtrusive long-term monitoring of EEG in real-life environments (Fiedler et al, 2016; Goverdovsky et al, 2016; Kappel, 2016; Kappel and Kidmose, 2018).…”

Section: Introductionmentioning

confidence: 99%

Ear-EEG Forward Models: Improved Head-Models for Ear-EEG

2019

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Computational models for mapping electrical sources in the brain to potentials on the scalp have been widely explored. However, current models do not describe the external ear anatomy well, and is therefore not suitable for ear-EEG recordings. Here we present an extension to existing computational models, by incorporating an improved description of the external ear anatomy based on 3D scanned impressions of the ears. The result is a method to compute an ear-EEG forward model, which enables mapping of sources in the brain to potentials in the ear. To validate the method, individualized ear-EEG forward models were computed for four subjects, and ear-EEG and scalp EEG were recorded concurrently from the subjects in a study comprising both auditory and visual stimuli. The EEG recordings were analyzed with independent component analysis (ICA) and using the individualized ear-EEG forward models, single dipole fitting was performed for each independent component (IC). A subset of ICs were selected, based on how well they were modeled by a single dipole in the brain volume. The correlation between the topographic IC map and the topographic map predicted by the forward model, was computed for each IC. Generally, the correlation was high in the ear closest to the dipole location, showing that the ear-EEG forward models provided a good model to predict ear potentials. In addition, we demonstrated that the developed forward models can be used to explore the sensitivity to brain sources for different ear-EEG electrode configurations. We consider the proposed method to be an important step forward in the characterization and utilization of ear-EEG.

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Section: Introductionmentioning

confidence: 99%

Ear-EEG Forward Models: Improved Head-Models for Ear-EEG

2019

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“…There are two large categories of motion artifacts (Cao et al, 2015). First, motions of the measurement system physically disturb the signal path, which can be converted to motion FIGURE 2 | History of bio-signals from its first experiment to modern commercialized products: (A) EEG (Blom and Anneveldt, 1982;g.tec, 2021;NeuroSky MindWave, 2021;Kappel, 2018) and (B) PPG (Nellcor, 2021;Nonin Medical, 2021;Tamura et al, 2014;Poh et al, 2010) artifacts.…”

Section: Understanding Of Electroencephalography Motion Artifactmentioning

confidence: 99%

Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

et al. 2021

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Removal of motion artifacts is a critical challenge, especially in wearable electroencephalography (EEG) and photoplethysmography (PPG) devices that are exposed to daily movements. Recently, the significance of motion artifact removal techniques has increased since EEG-based brain–computer interfaces (BCI) and daily healthcare usage of wearable PPG devices were spotlighted. In this article, the development on EEG and PPG sensor systems is introduced. Then, understanding of motion artifact and its reduction methods implemented by hardware and/or software fashions are reviewed. Various electrode types, analog readout circuits, and signal processing techniques are studied for EEG motion artifact removal. In addition, recent in-ear EEG techniques with motion artifact reduction are also introduced. Furthermore, techniques compensating independent/dependent motion artifacts are presented for PPG.

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“…The EEG devices used in clinical settings are bulky, require trained personnel, and are timeconsuming to setup and conduct measurements with. Hence, over the past decade, many research groups have investigated recording EEG from the outer ear canal (Ear-EEG) [1,3,4,8,14]. Ear-EEG has been shown to have similar features and quality to traditional scalp EEG [10].…”

Section: Introductionmentioning

confidence: 99%

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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Development and Characterization of Ear-EEG for Real-Life Brain-Monitoring

Cited by 6 publications

References 68 publications

Ear-EEG Forward Models: Improved Head-Models for Ear-EEG

Ear-EEG Forward Models: Improved Head-Models for Ear-EEG

Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

Earable Design Analysis for Sleep EEG Measurements

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