Saccade and Fix Detection from EOG Signal

Roy, Somnath; de, A.M.F.L.M.; Panigrahi, Narayan

doi:10.1109/ises47678.2019.00099

Cited by 5 publications

(6 citation statements)

References 8 publications

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“…Noise was also removed using median filtering. The EOG signal was particularly valuable because it kept Scud's extremely steep character [18].…”

Section: Signal Preprocessingmentioning

confidence: 99%

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Eye Movement Recognition Using Support Vector Machine

R. Abih,

J. Hayawi

2023

JEPS

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People with disabilities suffer from inability to communicate with their surroundings, so Human-Computer Interaction (HCI) technologies are used to have a means of communication for people withdisabilities with their surroundings. HCI is an emerging technology in the disciplines of ArtificialIntelligence and Biomedical Engineering. To power an external device, HCI technology uses severalbasic signals such as ECG, EMG, and EEG. Electrooculography (EOG) is a technique for measuring thepotential difference between the cornea and the retina located between the front and back of the humaneye, and the main application of EOG is to determine the directions of different eye movements. Thisstudy aims to assess eye movement for communication by persons with disabilities usingelectrocardiogram (EOG) data. In this study, the Supporting Vector Machine (SVM) classificationtechnique was used and two types of features (statistical and time domain features) were used.Classification accuracy was 90.7% and 93.9% when using SVM with statistical domain and time domainfeatures, respectively

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“…Noise was also removed using median filtering. The EOG signal was particularly valuable because it kept Scud's extremely steep character [18].…”

Section: Signal Preprocessingmentioning

confidence: 99%

“…In this study ten statistical features were extracted to present the characteristic of EOG signal. Those statistical feature are [18] :…”

Section: Statistical Featuresmentioning

confidence: 99%

Eye Movement Recognition Using Support Vector Machine

R. Abih,

J. Hayawi

2023

JEPS

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“…Roy et al [19] established a system for human beings cognitive capabilities understanding by investigating the saccadic movement and its pattern. The movement of the eyeball from one viewpoint to another is referred as saccade.…”

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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“…Since the first experiments of electroencephalography on humans in 1929, the electroencephalogram (EEG) of the human brain has been used mainly to evaluate neurological disorders in the clinical environment and to investigate brain functions in the laboratory. An idea that brain activity could be used as a communication channel between the subject and an IoT device has gradually emerged [2][3][4][5][6]. The possibility of recognizing a single message or command, considering the complexity, distortion, and variability of brain signals appeared to be extremely remote.…”

Section: Introductionmentioning

confidence: 99%

Design of an EEG Acquisition System for Embedded Edge Computing

Rai¹,

Thakur²,

Mane³

et al. 2020

Adv. sci. technol. eng. syst. j.

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The human brain is one of the most complex machines on the planet. Being the only method to get real-time data with high temporal resolution from the brain makes EEG a highly sought upon signal in the neurological and psychiatric domain. However, recent developments in this field have made EEG more than just a tool for medical professionals. The decreasing size and increasing complexity of EEG acquisition systems have brought it out of the lab and into the field where it is used for varied applications like neurofeedback, person recognition and other recreational activities. Amalgamation of the EEG signal with new developing standards of Industry 4.0 to control basic IOT devices using edge computing techniques marks the next step in the design and development our low-cost yet robust Brain Computer Interface (BCI); which is just one of the many applications that a versatile and well-built EEG acquisition system can be used for.

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Saccade and Fix Detection from EOG Signal

Cited by 5 publications

References 8 publications

Eye Movement Recognition Using Support Vector Machine

Eye Movement Recognition Using Support Vector Machine

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

Design of an EEG Acquisition System for Embedded Edge Computing

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