De-noising and Recognition of EOG Signal Based on Mathematical Morphology

Pan, Junjun; Zhou, Runjing

doi:10.1109/iscid.2013.201

Cited by 6 publications

(4 citation statements)

References 2 publications

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“…Contemporary traditional saccade detection and classification methods, which rely on current threshold-based techniques, necessitate EOG signals characterized by minimal noise levels to effectively discern abrupt changes in amplitude and velocity profiles [ 18 ]. To attain this objective, conventional signal processing techniques such as bandpass filters [ 18 , 19 ], wavelet transforms [ 20 , 21 , 22 ], and smoothing filters [ 14 , 15 ], as well as specialized filters like morphological filters [ 23 ] and dynamic time-warping filters [ 24 ], have been employed to denoise EOG signals. Nonetheless, these traditional filtering methods often introduce distortions by inadvertently diminishing peak velocities and extending saccade duration during the denoising process [ 14 , 15 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography

Gunawardane,

MacNeil,

Zhao

et al. 2024

Sensors

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Electrooculography (EOG) serves as a widely employed technique for tracking saccadic eye movements in a diverse array of applications. These encompass the identification of various medical conditions and the development of interfaces facilitating human–computer interaction. Nonetheless, EOG signals are often met with skepticism due to the presence of multiple sources of noise interference. These sources include electroencephalography, electromyography linked to facial and extraocular muscle activity, electrical noise, signal artifacts, skin-electrode drifts, impedance fluctuations over time, and a host of associated challenges. Traditional methods of addressing these issues, such as bandpass filtering, have been frequently utilized to overcome these challenges but have the associated drawback of altering the inherent characteristics of EOG signals, encompassing their shape, magnitude, peak velocity, and duration, all of which are pivotal parameters in research studies. In prior work, several model-based adaptive denoising strategies have been introduced, incorporating mechanical and electrical model-based state estimators. However, these approaches are really complex and rely on brain and neural control models that have difficulty processing EOG signals in real time. In this present investigation, we introduce a real-time denoising method grounded in a constant velocity model, adopting a physics-based model-oriented approach. This approach is underpinned by the assumption that there exists a consistent rate of change in the cornea-retinal potential during saccadic movements. Empirical findings reveal that this approach remarkably preserves EOG saccade signals, resulting in a substantial enhancement of up to 29% in signal preservation during the denoising process when compared to alternative techniques, such as bandpass filters, constant acceleration models, and model-based fusion methods.

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Section: Introductionmentioning

confidence: 99%

A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography

Gunawardane,

MacNeil,

Zhao

et al. 2024

Sensors

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“…In addition, during a long EOG recording session, the recorded EOG signals are usually distorted by noise caused by the high impedance existed between the skin and electrode due to the sweat and skin oil. Therefore, it is difficult to identify EOG signals without De-noising stage that minimizes the interference (Jiang & Zhou, 2013). Several of De-noise methods have been suggested to enhance the accuracy in recognizing eye movements.…”

Section: Introductionmentioning

confidence: 99%

“…Several of De-noise methods have been suggested to enhance the accuracy in recognizing eye movements. Mathematical morphology filtering method has been recommended to process the EOG signals in time domain to achieve average recognition rate of 93% (Jiang & Zhou, 2013). Moreover, stationary wavelet transform with hard/soft thresholding method is proposed to Denoise unprocessed EOG signals (Rajesh A, S., T., & K., 2012).…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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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