A Method of EOG Signal Processing to Detect the Direction of Eye Movements

Merino, Manuel; Rivera, Octavio; Gómez, I.; Molina, Alberto; Dorronzoro, Enrique

doi:10.1109/sensordevices.2010.25

Cited by 61 publications

(34 citation statements)

References 5 publications

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“…filter, a band-pass filter (cut-off frequencies o was programmed in LabVIEW for better filte signal. A high-pass filter of cutoff frequency 0 eliminate DC offset [5] and a low-pass filter of 35 Hz was used to band limit the EOG signal of the signal was obtained using a low pas frequency 3 Hz [10] (Figure 7).…”

Section: A Eog Signal Processing In Labviewmentioning

confidence: 99%

Development of EOG based human machine interface control system for motorized wheelchair

Champaty¹,

Jose²,

Pal

et al. 2014

2014 Annual International Conference on Emerging Research Areas: Magnetics, Machines and Drives (AICERA/iCMMD)

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Rehabilitation devices are increasingly being used to improve the quality of the life of differentially-abled persons. Human Machine Interface (HMI) has been extensively studied to control electromechanical rehabilitation aids using biosignals. Electroocculogram (EOG) signal is one of the most commonly studied signals due to the occurrence of definite signal patterns. Even the persons suffering from extremely limited peripheral mobility conditions (e.g. paraplegia or quadriplegia) have the ability to coordinate eye movements. The current project focuses on the development of an EOG based control system for driving a prototype wheelchair model. As a part of this work, an EOG signal acquisition system was developed. The acquired EOG signal was then processed to generate control signals (depending on the amplitude and duration of signals) for the movement of the wheelchair model.

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Section: A Eog Signal Processing In Labviewmentioning

confidence: 99%

Development of EOG based human machine interface control system for motorized wheelchair

Champaty¹,

Jose²,

Pal

et al. 2014

2014 Annual International Conference on Emerging Research Areas: Magnetics, Machines and Drives (AICERA/iCMMD)

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“…Three electrodes on the arm were used for the EMG signal (figure 2.c). Finally, to pick EOG signal up two electrodes by direction (figure 2.d) where placed around the eye [13] and the ground and reference sensors were placed respectively in the right and left ears. Ag/AgCl electrodes with self-adhesive/conductive gel were used to record the biosignals.…”

Section: Signal Acquisitionmentioning

confidence: 99%

Assessment of Biosignals for Managing a Virtual Keyboard

Merino

Gómez

Molina

et al. 2012

Lecture Notes in Computer Science

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Abstract. In this paper we propose an assessment of biosignals for handling an application based on virtual keyboard and automatic scanning. The aim of this work is to measure the effect of using such application, through different interfaces based on electromyography and electrooculography, on cardiac and electrodermal activities. Five people without disabilities have been tested. Each subject wrote twice the same text using an electromyography interface in first test and electrooculography in the second one. Each test was divided into four parts: instruction, initial relax, writing and final relax. The results of the tests show important differences in the electrocardiogram and electrodermal activity among the parts of tests.

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“…Furthermore, the effectiveness of this method depends on the type of wavelet and decomposition wavelet (Mala & Latha, 2016). Most of the recent algorithms of eye movement detection are based on differential method (Merino, Rivera, Gómez, Molina, & Dorronzoro, 2010) (Jiang & Zhou, 2013). In other words, the derivative of the EOG signal is considered to detect eye movements.…”

Section: Introductionmentioning

confidence: 99%

Simple and Robust Eye Movements Detection Method

Samann

2017

juod

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The aim of this research is to develop a signal processing method to detect four eye movements, such as looking up, down, left, right and blinking. This new method has a couple of features in comparison with the recent eye movement detection algorithms. Most of the recent algorithms require a De-noising stage, which is not required in this work. In addition, the suggested algorithm can be considered simple and robust in noisy environment in contrast with other algorithms. In this paper, short-time averaging method is proposed to process and to extract parameters from EOG signals. Moreover, adaptive threshold is applied to classify EOG pulses. The purpose of the adaptive threshold is to enhance the performance of the algorithm in a noisy background. Simulation results are based on real-life EOG signals, where these signals were recorded using an Electrooculography system. The results show that the proposed algorithm has a stable performance HR=100 % and FR=0% with SNR greater than 2dB. The average performance with SNR=0.5 dB is about HR= 90.21% and FR= 4.88%.

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A Method of EOG Signal Processing to Detect the Direction of Eye Movements

Cited by 61 publications

References 5 publications

Development of EOG based human machine interface control system for motorized wheelchair

Development of EOG based human machine interface control system for motorized wheelchair

Assessment of Biosignals for Managing a Virtual Keyboard

Simple and Robust Eye Movements Detection Method

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