The future of wearable EEG: a review of ear-EEG technology and its applications

Kaongoen, Netiwit; Choi, Jaehoon; Woo Choi, Jin; Kwon, Haram; Hwang, Chaeeun; Hwang, Guebin; Kim, Byung Hyung; Jo, Sungho

doi:10.1088/1741-2552/acfcda

Cited by 15 publications

(10 citation statements)

References 134 publications

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“…The ears seem to be a suitable location for capturing biosignals, as demonstrated in this work, where we use multi-lead SoftPulse TM earplugs with six integrated leads, as well as from the literature [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 18 ]. When referenced to another measurement point on or below the neck, it could be possible to capture a high-quality bipolar ECG between the ear and the other measurement point.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to its potential applications in mobile health, in-ear signal capture has also gained interest for human–machine interface (HMI) applications [ 2 ]. In-ear dry electrodes can capture various physiological signals, such as electrocardiography (ECG), electrooculography (EOG), electromyography (EMG), and electroencephalography (EEG) [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ], which can be used, for example, for sleep stage analysis and to detect and classify cognitive and emotional states. This has the potential to enhance the interaction between humans and machines in various fields, including gaming, virtual reality, and neuroprosthetics [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

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 recent years, the practicality of wearable devices has become increasingly evident across numerous domains, such as industry, medicine, military, education, and entertainment [1][2][3]. Wearable devices designed for health monitoring purposes record various physiological signals, including electromyography (EMG) [4], electrocardiography (ECG) [5], electroencephalography (EEG) [6], and so on. Among these, EEG-based wearable devices have garnered substantial attention due to their versatility in a wide range of applications.…”

Section: Introductionmentioning

confidence: 99%

A feature enhanced EEG compression model using asymmetric encoding–decoding network ^*

Wang,

Zhang,

2024

J. Neural Eng.

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Objective. Recently, the demand for wearable devices using electroencephalography (EEG) has increased rapidly in many fields. Due to its volume and computation constraints, wearable devices usually compress and transmit EEG to external devices for analysis. However, current EEG compression algorithms are not tailor-made for wearable devices with limited computing and storage. Firstly, the huge amount of parameters makes it difficult to apply in wearable devices; secondly, it’s tricky to learn EEG signals’ distribution law due to the low signal-to-noise ratio, which leads to excessive reconstruction error and suboptimal compression performance. Approach. Here, a feature enhanced asymmetric encoding-decoding network is proposed. EEG is encoded with a lightweight model, and subsequently decoded with a multi-level feature fusion network by extracting the encoded features deeply and reconstructing the signal through a two-branch structure. Main results. On public EEG datasets, motor imagery and event-related potentials, experimental results show that the proposed method has achieved the state of the art compression performance. In addition, the neural representation analysis and the classification performance of the reconstructed EEG signals also show that our method tends to retain more task-related information as the compression ratio increases and retains reliable discriminative information after EEG compression. Significance. This paper tailors an asymmetric EEG compression method for wearable devices that achieves state-of-the-art compression performance in a lightweight manner, paving the way for the application of EEG-based wearable devices.

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“…Though implantable EEG devices are hardly or not-at-all visible once surgery is accomplished [ 6 , 7 ], due to their invasiveness they are highly likely to remain restricted to patients suffering from pharmaco-resistant epilepsies for the foreseeable future. Therefore, it is important to design EEG systems that are both non-invasive and less stigmatizing [ 8 , 9 , 10 ]. Stigmatization may be reduced by integrating the device into objects of daily use or restricting recordings to nighttime sleep when patients are not socially exposed.…”

Section: Introductionmentioning

confidence: 99%

Ultra-Long-Term-EEG Monitoring (ULTEEM) Systems: Towards User-Friendly Out-of-Hospital Recordings of Electrical Brain Signals in Epilepsy

Yilmaz,

Seiler,

Chételat

et al. 2024

Sensors

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Epilepsy is characterized by the occurrence of epileptic events, ranging from brief bursts of interictal epileptiform brain activity to their most dramatic manifestation as clinically overt bilateral tonic–clonic seizures. Epileptic events are often modulated in a patient-specific way, for example by sleep. But they also reveal temporal patterns not only on ultra- and circadian, but also on multidien scales. Thus, to accurately track the dynamics of epilepsy and to thereby enable and improve personalized diagnostics and therapies, user-friendly systems for long-term out-of-hospital recordings of electrical brain signals are needed. Here, we present two wearable devices, namely ULTEEM and ULTEEMNite, to address this unmet need. We demonstrate how the usability concerns of the patients and the signal quality requirements of the clinicians have been incorporated in the design. Upon testbench verification of the devices, ULTEEM was successfully benchmarked against a reference EEG device in a pilot clinical study. ULTEEMNite was shown to record typical macro- and micro-sleep EEG characteristics in a proof-of-concept study. We conclude by discussing how these devices can be further improved and become particularly useful for a better understanding of the relationships between sleep, epilepsy, and neurodegeneration.

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The future of wearable EEG: a review of ear-EEG technology and its applications

Cited by 15 publications

References 134 publications

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

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

A feature enhanced EEG compression model using asymmetric encoding–decoding network ^*

Ultra-Long-Term-EEG Monitoring (ULTEEM) Systems: Towards User-Friendly Out-of-Hospital Recordings of Electrical Brain Signals in Epilepsy

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The future of wearable EEG: a review of ear-EEG technology and its applications

Cited by 15 publications

References 134 publications

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

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

A feature enhanced EEG compression model using asymmetric encoding–decoding network *

Ultra-Long-Term-EEG Monitoring (ULTEEM) Systems: Towards User-Friendly Out-of-Hospital Recordings of Electrical Brain Signals in Epilepsy

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A feature enhanced EEG compression model using asymmetric encoding–decoding network ^*