EOG-Based Human–Computer Interface: 2000–2020 Review

Belkhiria, Chama; Boudir, Atlal; Hurter, Christophe; Peysakhovich, Vsevolod

doi:10.3390/s22134914

Cited by 16 publications

(7 citation statements)

References 142 publications

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“…Consequently, apart from high-pass filtering, notch filtering, and blink removal algorithm, no further data cleaning is applied. The majority of EOG-HMI based studies use a band-pass filter between 0.1 -30 Hz [13] and are unable to utilize higher frequency harmonic information. Nonetheless, by removing the percussive components from the basic EOG signal, harmonic signals in higher frequency bands are recovered.…”

Section: Discussionmentioning

confidence: 99%

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Single-channel EOG-based human-machine interface with exploratory assessments using harmonic source separation

Demirel¹,

Reguş²,

Köse³

2022

Preprint

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There have been many studies on intelligent robotic systems for patients with motor impairments, where different sensor types and different human-machine interface (HMI) methods have been developed. However, these studies fail to achieve complex activity detection at the minimum sensing level. In this paper, exploratory approaches are adopted to investigate ocular activity dynamics and complex activity estimation using a single-channel EOG device. First, the stationarity of ocular activities during a static motion is investigated and some activities are found to be non-stationary. Further, no statistical difference is found between the envelope sequences in the temporal domain. However, when utilized as an alternative to a low-pass filter, high-frequency harmonic components in the frequency domain are found to improve contrasting ocular activities and the performance of the EOG-HMI-based activity detection system substantially. The activities are trained with different classifiers and their prediction success is evaluated with leave-one-session-out cross-validation. Accordingly, the two-dimensional CNN model achieved the highest performance with the accuracy of 72.35%. Furthermore, the clustering performance is assessed using unsupervised learning and the results are evaluated in terms of how well the feature sets are grouped. The system is further tested in real-time with the graphical user interface and the scores and survey data of the subjects are used to verify the effectiveness.

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Section: Discussionmentioning

confidence: 99%

“…Further, the majority of studies developing EOG-HMI based systems have developed decision making models based on blink and saccadic potentials [13]. As a result of these findings relying on EOG time-domain potential oriented rulebased systems, complex ocular activity recognition haven't been developed using single-channel EOG signal.…”

Section: Introductionmentioning

confidence: 99%

Single-channel EOG-based human-machine interface with exploratory assessments using harmonic source separation

Demirel¹,

Reguş²,

Köse³

2022

Preprint

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“…Additionally, the EMG signals captured from hand motions can be effectively employed to enable synchronous gameplay of the snake game (Figure 7a). [55,56] By utilizing distinguishable biopotential signals captured through our electrodes, a control system based on human activity has been developed. The system's process of collecting bioelectric signals for human-computer interaction is illustrated in Figure 7b.…”

Section: Application Of the Kirigami-structured Pedot:pss/pva/ag Nws/...mentioning

confidence: 99%

Kirigami‐Structured, Low‐Impedance, and Skin‐Conformal Electronics for Long‐Term Biopotential Monitoring and Human–Machine Interfaces

Xia,

Liu,

Kim

et al. 2023

Advanced Science

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Epidermal dry electrodes with high skin‐compliant stretchability, low bioelectric interfacial impedance, and long‐term reliability are crucial for biopotential signal recording and human–machine interaction. However, incorporating these essential characteristics into dry electrodes remains a challenge. Here, a skin‐conformal dry electrode is developed by encapsulating kirigami‐structured poly(3,4‐ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS)/polyvinyl alcohol (PVA)/silver nanowires (Ag NWs) film with ultrathin polyurethane (PU) tape. This Kirigami‐structured PEDOT:PSS/PVA/Ag NWs/PU epidermal electrode exhibits a low sheet resistance (≈3.9 Ω sq−1), large skin‐compliant stretchability (>100%), low interfacial impedance (≈27.41 kΩ at 100 Hz and ≈59.76 kΩ at 10 Hz), and sufficient mechanoelectrical stability. This enhanced performance is attributed to the synergistic effects of ionic/electronic current from PEDOT:PSS/Ag NWs dual conductive network, Kirigami structure, and unique encapsulation. Compared with the existing dry electrodes or standard gel electrodes, the as‐prepared electrodes possess lower interfacial impedance and noise in various conditions (e.g., sweat, wet, and movement), indicating superior water/motion‐interference resistance. Moreover, they can acquire high‐quality biopotential signals even after water rinsing and ultrasonic cleaning. These outstanding advantages enable the Kirigami‐structured PEDOT:PSS/PVA/Ag NWs/PU electrodes to effectively monitor human motions in real‐time and record epidermal biopotential signals, such as electrocardiogram, electromyogram, and electrooculogram under various conditions, and control external electronics, thereby facilitating human–machine interactions.

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“…However, the accuracy of the obtained data is limited if the data are collected only by eye trackers or EOG measurements. Multimodal eye movement signal recording methods that incorporate EEG, EMG, and eye trackers can significantly improve human-machine interface system performance [ 122 , 123 ].…”

Section: Eye Movementmentioning

confidence: 99%

Emerging Wearable Biosensor Technologies for Stress Monitoring and Their Real-World Applications

Ching

Wang

et al. 2022

Biosensors

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Wearable devices are being developed faster and applied more widely. Wearables have been used to monitor movement-related physiological indices, including heartbeat, movement, and other exercise metrics, for health purposes. People are also paying more attention to mental health issues, such as stress management. Wearable devices can be used to monitor emotional status and provide preliminary diagnoses and guided training functions. The nervous system responds to stress, which directly affects eye movements and sweat secretion. Therefore, the changes in brain potential, eye potential, and cortisol content in sweat could be used to interpret emotional changes, fatigue levels, and physiological and psychological stress. To better assess users, stress-sensing devices can be integrated with applications to improve cognitive function, attention, sports performance, learning ability, and stress release. These application-related wearables can be used in medical diagnosis and treatment, such as for attention-deficit hyperactivity disorder (ADHD), traumatic stress syndrome, and insomnia, thus facilitating precision medicine. However, many factors contribute to data errors and incorrect assessments, including the various wearable devices, sensor types, data reception methods, data processing accuracy and algorithms, application reliability and validity, and actual user actions. Therefore, in the future, medical platforms for wearable devices and applications should be developed, and product implementations should be evaluated clinically to confirm product accuracy and perform reliable research.

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EOG-Based Human–Computer Interface: 2000–2020 Review

Cited by 16 publications

References 142 publications

Single-channel EOG-based human-machine interface with exploratory assessments using harmonic source separation

Single-channel EOG-based human-machine interface with exploratory assessments using harmonic source separation

Kirigami‐Structured, Low‐Impedance, and Skin‐Conformal Electronics for Long‐Term Biopotential Monitoring and Human–Machine Interfaces

Emerging Wearable Biosensor Technologies for Stress Monitoring and Their Real-World Applications

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