Hardware and software implementation of real time electrooculogram (EOG) acquisition system to control computer cursor with eyeball movement

Hossain, Zakir; Shuvo, Md. Maruf Hossain; Sarker, Prionjit

doi:10.1109/icaee.2017.8255341

Cited by 27 publications

(12 citation statements)

References 7 publications

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“…The result proves that TDNN model was higher than the other network model used in this study [25]. Hossain et al [26] developed eye controlled cursor movements using Support Vector machine and Linear Discriminant Analysis.…”

Section: Previous Studymentioning

confidence: 52%

Prospects of Electrooculography in Human-Computer Interface Based Neural Rehabilitation for Neural Repair Patients

et al. 2019

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Disable persons are facing a lot of problems in daily life activities. They need some help from others to fulfil their needs every day. To avoid this condition modern technology help such persons to overcome the problem in a natural way like bio signal based-human-computer interaction. In this paper, we focused to study the performance of male subjects compared with female subjects to analyze the performance to design Electrooculographgy-based HCI using periodogram and neural network. Five male subjects and five female subjects are involved in this experiment. From the experimental analysis, we identified that male performance was maximum compared to female performance. From this paper, we analyzed that subject S4 from male subjects and subject S10 from female subjects performance was marginally high compared with other subjects performance took part in this experiment. From the classification accuracy, we conclude that male subject performance was encouraged with 93.67 % and 92.28% for female subjects. The offline test was conducted in the indoor environment to identify the tasks to confirm the performance of individual subjects. From the offline analysis, we conclude that subject S4 performance was high compared to other subjects take part in this paper. Subject S4 took less time to perform the task as per the protocol. Through this paper, we confirm that scheming HCI is achievable. INDEX TERMS Electrooculography, periodogram, human-computer interface, probabilistic neural network.

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Section: Previous Studymentioning

confidence: 52%

Prospects of Electrooculography in Human-Computer Interface Based Neural Rehabilitation for Neural Repair Patients

et al. 2019

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“…Linear Discriminant Analysis (LDA) and SVM module is used for the classification of Horizontal and vertical data which helps in cursor movement. 39 C.T. Lin and et al proposed an application on HCI Baseball Game using eye movement classification.…”

Section: Discussionmentioning

confidence: 99%

Non-invasive technique to detect diabetic retinopathy based on Electrooculography signal using machine learning classifiers

Archana

Rajalakshmi

2022

Proc Inst Mech Eng H

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Single-channel Electrooculogram (EOG) is proposed for detecting diabetic retinopathy. The Corneal-retinal potential of the eyes plays a vital role in the acquisition of Electrooculography. Diabetes is the most prevalent disease and for one out of three people with diabetes above 40 years, diabetic retinopathy occurs. It is necessary for the early detection of diabetic retinopathy as it is one of the primary reasons for blindness in the population. The potential difference between cornea and retina leads to the acquisition of EOG signal. The proposed study aims to design a low-cost miniaturized hardware circuit to obtain EOG signal using second order filters without compromising in accuracy of the outcome signal and to classify the signal into normal and diabetic retinopathy subjects by extracting the statistical features like kurtosis, mean, median absolute deviation, standard deviation, and range from software filtered EOG signal. Among the classifiers used, Support vector machine (SVM) shows a higher accuracy of 93.33%. The sensitivity, specificity and Area Under Curve (AUC) values of SVM are 96.43%, 90.625%, 0.93% is considered as more favorable outcome for the proposed method and it supports the developed prototype and processing methodology. The novelty of the research is based on proposing and exploring a non-invasive methodology for Diabetic retinopathy diagnosis based on EOG signal. Thus, the designed hardware is simple in operation and cost effective, provides an affordable and non-invasive diagnostic tool for diabetic retinopathy patients

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“…In the past few years, we have seen an exponential growth in the development of Human-Computer Interface (HCI) systems. These systems have been applied for a wide range of purposes like controlling a computer cursor [1], a virtual keyboard [2], a prosthesis [3], or a wheelchair [4][5][6][7]. They could also be used for patient rehabilitation and communication [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…One of the issues with current HCI systems is their size and lack of autonomy, the use of proprietary software, or being based on self-designed acquisition and processing devices. Regarding the acquisition system, the most common approach is to use a self-designed acquisition device [1,4,[20][21][22]. In our view, this solution dramatically restricts the number of users who can adopt this system.…”

mentioning

confidence: 99%

“…This also reduces the number of potential users of the system and its applications since it increases the cost of the system and reduces its flexibility, portability, and autonomy. As far as signal processing is concerned, most systems choose to use a laptop to carry out these calculations [1,[20][21][22]24,25], but we can also find the use of self-designed boards [6,26]. Table 1 shows the characteristics of some solutions present in literature as a representation of the current state of the art.…”

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confidence: 99%

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Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification

Martínez-Cerveró

Khalili-Ardali

Jaramillo-Gonzalez

et al. 2020

Sensors

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Electrooculography (EOG) signals have been widely used in Human-Computer Interfaces (HCI). The HCI systems proposed in the literature make use of self-designed or closed environments, which restrict the number of potential users and applications. Here, we present a system for classifying four directions of eye movements employing EOG signals. The system is based on open source ecosystems, the Raspberry Pi single-board computer, the OpenBCI biosignal acquisition device, and an open-source python library. The designed system provides a cheap, compact, and easy to carry system that can be replicated or modified. We used Maximum, Minimum, and Median trial values as features to create a Support Vector Machine (SVM) classifier. A mean of 90% accuracy was obtained from 7 out of 10 subjects for online classification of Up, Down, Left, and Right movements. This classification system can be used as an input for an HCI, i.e., for assisted communication in paralyzed people.

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Hardware and software implementation of real time electrooculogram (EOG) acquisition system to control computer cursor with eyeball movement

Cited by 27 publications

References 7 publications

Prospects of Electrooculography in Human-Computer Interface Based Neural Rehabilitation for Neural Repair Patients

Prospects of Electrooculography in Human-Computer Interface Based Neural Rehabilitation for Neural Repair Patients

Non-invasive technique to detect diabetic retinopathy based on Electrooculography signal using machine learning classifiers

Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification

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