EOG-based eye tracking protocol using baseline drift removal algorithm for long-term eye movement detection

Ryu, Jaehwan; Lee, Miran; Kim, Deok‐Hwan

doi:10.1016/j.eswa.2019.04.039

Cited by 21 publications

(21 citation statements)

References 19 publications

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“…Therefore, if lengths of the short axis and the long axis of the PPC are known, RSA can be calculated according to (5). Thus, the three-dimensional position of the PPC can be approximated by the center point of the PCP and the RSA Fig.…”

Section: D Rotation Of the Pupil Discmentioning

confidence: 99%

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A study of Human pupil orbit model estimation method for the eye-gaze tracking.

Lee

Jeong

Kim

et al. 2022

Preprint

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Background: Eye tracking is a technology that detects the direction of the user's gaze in real time. This technology is used in various fields such as analyzing user emotions and behaviors by analyzing the length of time the gaze stays in a specific direction or the path of movement. Recently, Eye-gaze tracking is a popular to a non-contact interaction technique for the minimization of covid-19 infection. A study conducted in the past have a limitation in that the ocular rotational center point (ORCP) cannot be found in common. For this reason, each research is continuously researching to overcome this problem by using various sensors, structures, and phenomena. In spite of, a tiny movement of the subject cause an error in accuracy during the process of Eye-gaze tracking. In this paper, we propose a novel method for Eye-gaze tracking under the condition of photographing the front of the face using a camera. We named this position set as Human pupil orbit model (HPOM) and it can express as a spherical equation because it looks like a sphere in 3-dimensional coordinate.Results: A simulation experiment was performed to confirm the performance of the proposed method. The error of HPOM estimation appears under 1 pixel in the first experiment both ORCP and a radius. In addition, it was confirmed that the process of estimating HPOM can be conducted as a real time in the second experiment.Conclusions: The proposed method was able to overcome the common problem of existing studies that could not find the rotational center of an eye-ball. In addition, HPOM may be secured in real time even if the subject moves.

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Section: D Rotation Of the Pupil Discmentioning

confidence: 99%

“…One is using electrodes and another one is using a camera. The method of using electrodes tracks the gaze by attaching electrodes around the eyes and analyzing the signals generated by eye movements [5]. The method of using a camera is performed by photographing close-up eyes or photographing the front of the face and analyzing it.…”

Section: Introductionmentioning

confidence: 99%

A study of Human pupil orbit model estimation method for the eye-gaze tracking.

Lee

Jeong

Kim

et al. 2022

Preprint

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“…Ryu et al [23] introduced a novelty in eliminating the baseline drift and noise by deploying a differential electrooculography (EOG) signal established on a fixation curve (DOSbFC) and a new electrode positioning structure constructed on eyeglasses. The human-computer interface is managed by implementation of desktop and mobile applications.…”

Section: Related Workmentioning

confidence: 99%

Accurate and Efficient Differentiation Between Normal and Epileptic Seizure of Eyes Using 13 Layer Convolution Neural Network

Brahmaiah¹,

Yarlagadda²,

Prasad³

2021

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Epileptic seizure is one which affects the normal brain activities of human being and considered to be a risky disease. The eye ball movement signals pattern plays a significant role in determining the epileptic seizure in precise manner. In addition to it, EOG signals has its influence in detecting epileptic seizure through assessment of eye ball movement signals precisely. Detecting Epilepsy using genetical based Convolutional Neural Network plays a major role in the previous research works. Conversely, the existence of background noise on eye ball signals may impact on the outcome failure. Noise aware Epileptic Seizure Detection using Thirteen Layer Convolution Neural Network (NESD-TLCNN) is adopted in this research to mitigate this issue and thereby ensuring the prediction rate more precisely. Furthermore, Hybrid Dynamic Time Wrapping based Hidden Markov Model (HDWT-HMM) is greatly utilized for primary background noise detection and removal by estimating the noise depending on distance metric. Once after the completion of noise estimation, perfect detection of epileptic seizure is accomplished using feature extraction. The peculiar features involved are saccade, fixation and blink features. Subsequently, Particle swarm optimization (PSO) technique is also involved in this research for optimal feature selection. Thirteen Layer Convolution Neural Network (TLCNN) is applied at last for learning and differentiation of epileptic seizure from the normal eyes. This research is being carried out in MATLAB platform which also reveals that the anticipated methodology produces improved outcomes when contrasted with the existing research work.

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“…However, a clear pattern in the alpha band of the EEG signal can be seen from a relaxed waked human when the eyes are closed [58], [59]. Eye blinking or Electro-oculographic (EOG) can also be captured easily since it generates a strong signal compared to EEG [60], [61]. An electrode was placed at the O1 occipital region to record the alpha wave while the reference and ground electrodes were connected to the Pz location and left earlobe respectively.…”

Section: E Alpha Waves and Eye Blinksmentioning

confidence: 99%

High Performance 128-Channel Acquisition System for Electrophysiological Signals

et al. 2020

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The increased popularity of investigations and exploits in the fields of neurological rehabilitation, human emotion recognition, and other relevant brain-computer interfaces demand the need for flexible electrophysiology data acquisition systems. Such systems often require to be multi-modal and multi-channel capable of acquiring and processing several different types of physiological signals simultaneously in realtime. Developments of modular and scalable electrophysiological data acquisition systems for experimental research enhance understanding and progress in the field. To contribute to such an endeavor, we present an open-source hardware project called High-Channel Count Electrophysiology or HiCCE, targeting to produce an easily-adaptable, cost-effective, and affordable electrophysiological acquisition system as an alternative solution for mostly available commercial tools and the current state of the art in the field. In this paper, we describe the design and validation of the entire chain of the HiCCE-128 electrophysiological data acquisition system. The system comprises of 128 independent channels capable of acquiring signal at 31.25 kHz, with 16 effective bits per channel with a measured noise level of about 3 µV. The reliability and feasibility of the implemented system have been confirmed through a series of tests and realworld applications. The modular design methodology based on the FPGA Mezzanine Card (FMC) standard allows the connection of the HiCCE-128 board to programmable system-on-chip carrier devices through the high-speed FMC link. The implemented architecture enables end users to add various high-response electrophysiological signal processing techniques in the field programmable gate arrays (FPGA) part of the system on chip (SoC) device on each channel in parallel according to application specification.

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EOG-based eye tracking protocol using baseline drift removal algorithm for long-term eye movement detection

Cited by 21 publications

References 19 publications

A study of Human pupil orbit model estimation method for the eye-gaze tracking.

A study of Human pupil orbit model estimation method for the eye-gaze tracking.

Accurate and Efficient Differentiation Between Normal and Epileptic Seizure of Eyes Using 13 Layer Convolution Neural Network

High Performance 128-Channel Acquisition System for Electrophysiological Signals

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