Implementation of the EOG-Based Human Computer Interface System

Lv, Zhao; Wu, Xiaopei; Li, Mi; Zhang, Chao

doi:10.1109/icbbe.2008.877

Cited by 44 publications

(20 citation statements)

References 6 publications

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“…The voltage for the horizontal eye movement is up to 16µV whereas it is 14µV for the vertical movement of the eye per 1° [1]. It is very clear from the literature that brain signals and power line interferences are noted in the recorded EOG signal and elimination of such contaminations is important for quality diagnosis.…”

Section: Introduction He Electrooculogram (Eog) Is a Graphic Record Omentioning

confidence: 99%

Denoising EOG Signal using Stationary Wavelet Transform

Naga¹,

Chandralingam²,

Anjaneyulu³

et al. 2012

Measurement Science Review

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Eye movements are critical signs of the neurological disorders and they can be acquired by EOG. The EOG signal is electrical signal generated due to eye ball movements and is contaminated with brain signals and power line while recording. As the EOG signal is a non-stationary signal, it can be denoised by wavelet transformation techniques. The present work covers denoising of noisy EOG signal using Stationary Wavelet Transform (SWT), which was done with all suitable wavelets that are morphologically similar to an EOG signal by applying both Soft and Hard Thresholding methods. An EOG signal was simulated and added with noise to obtain noisy EOG signal. The wavelet analysis of the simulated noisy EOG signal reveals that the Biorthogonal 3.3 wavelet is the best wavelet to denoise by using SWT technique, wherein the yield achieved was good with Signal to Noise Ratio of 36.5882 dB and minimum Mean Square Error of 0.383313 for quality diagnosis.

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Section: Introduction He Electrooculogram (Eog) Is a Graphic Record Omentioning

confidence: 99%

Denoising EOG Signal using Stationary Wavelet Transform

Naga¹,

Chandralingam²,

Anjaneyulu³

et al. 2012

Measurement Science Review

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show abstract

“…Using horizontal and vertical eye movements and two and three blinking signals a movable robot is controlled. Because the EOG signals are slightly different for the each subject, a dynamical threshold algorithm is developed (Lv, Wu, Li, & Zhang, 2008). In this approach, the initial threshold is compared with the dynamic range; the threshold value is renewed after each difference.…”

Section: Introductionmentioning

confidence: 99%

Development of Mouse Cursor Control System using DC and AC Elements of Electrooculogram Signals and its Applications

Tamura

Yan

Miyashita

et al. 2013

International Journal of Intelligent Computing in Medical Scien

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The aim of this study is to present electrooculogram signals that can be used for human computer interface efficiently. Establishing an efficient alternative channel for communication without overt speech and hand movements is important to increase the quality of life for patients suffering from Amyotrophic Lateral Sclerosis or other illnesses that prevent correct limb and facial muscular responses. Using electrooculogram signals, it is possible to improve the communication abilities of those patients who can move their eyes.In this paper, we introduce the mouse cursor control system for Amyotrophic Lateral Sclerosis patients using electrooculogram signals. We propose the algorithm using alternating current and direct current of electrooculogram signals corresponding to the baseline drift problem. The focus of our proposal system was laid on noise, baseline drift and eye blink artifact removal. In order to test the effectiveness of our proposal system, we tried the experiments of the Alphabet sentence input task using our proposal system.

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“…They are generally includes eyeball rotation and movement, eyelid movement shown in fig.1. EMG signal produced by the muscles of the eye, eye blinks, electrode placement and head movements [2]. To eliminate unwanted signals from biomedical signals, multi resolution based wavelet analysis has been extensively used in past decades [3].…”

Section: Introductionmentioning

confidence: 99%

On the performance of wavelet transform improving Eye blink detections for BCI

Sammaiah¹,

Baddiri²,

Suresh³

et al. 2011

2011 International Conference on Emerging Trends in Electrical and Computer Technology

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Detection and determination of Eye blinks from Electro-occulography Signals are very helpful in Brain Computer Interfacing (BCI) Systems such as wheel chair controlling, cursor controlling and home automations. Eye ball movements are vital signs in some of the neurological disorders and represents the electrical activity of the muscles which steering the eye movements. EOG is an obtrusive, inexpensive and non-invasive means of recording eye ball movements. The source for EOG signal is cornea-retinal potential (CRP) and is generated due to the movements of eye balls within the conductive environment of the skull. While recording the EOG signal, it will be contaminated by electromyography (EMG) signal. As the EOG is a non stationary signal, the multi resolution analysis using wavelet decomposition offers the best solution to denoise and feature extraction of EOG signals. In this paper, proposed wavelet based method to detect eye ball moments from signal conditioned EOG. Eye links are detected with high sensitivity and specificity. Comparative wavelet analysis is performed by considering different statistical measures.

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Implementation of the EOG-Based Human Computer Interface System

Cited by 44 publications

References 6 publications

Denoising EOG Signal using Stationary Wavelet Transform

Denoising EOG Signal using Stationary Wavelet Transform

Development of Mouse Cursor Control System using DC and AC Elements of Electrooculogram Signals and its Applications

On the performance of wavelet transform improving Eye blink detections for BCI

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