Design of a robust method to acquire EOG signals using Bio-medical signal processing

Raman, Suraj Kiran; Jayaram, Jayadev Kumar; Murugan, Senthil Kumar; Saha, Arnab; Kavitha, R.

doi:10.1109/get.2016.7916631

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…3) Electro-Oculogram signals are captured by placing electrodes on the frontal lobe of the head, driven by signal acquisition circuit comprises of high gain instrumentation amplifier having appropriate common mode rejection ratio, filter configurations to minimize noise and maintain signal to noise ratio. This signal are purposed to control the direction of camera lens [1]. Therefore appearance of image out of pre-trained model will be computed to determine and assign with appropriate notations and category [4].…”

Section: Methodsmentioning

confidence: 99%

Vision substitution using audio-tactual sensation

Damodar¹,

Suthar²,

Solanki³

2018

IJATEE

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Vision is integral part of Human body plays pivotal role in vision, reacting and balancing of entire body. Encountering loss of vision begin with refractory errors consequences in certain disorders like retinal detachment, glaucoma, cataract, tumors which might have detrimental impact on individual if not cure on time. Probably they need spectrum if only suffering with partial vision impairment but ophthalmic surgeries are needed where individual is entirely blind and unable to percept anything. Traditional innovations propose use of braille, tactile substitution, ultrasonic cane, laser cane to guide blind people to estimate about obstacle encounter in their pathway considerably classified as object comes in short sights or nearby circumstances and has poor or negligible performance for visualize far-sight scenario. It only alerts about obstacles while exempting details of that shape, size is in appearance.

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Section: Methodsmentioning

confidence: 99%

Vision substitution using audio-tactual sensation

Damodar¹,

Suthar²,

Solanki³

2018

IJATEE

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“…European Journal of Theoretical and Applied Sciences, 1(6), 904-915. DOI: 10.59324/ejtas.2023.1(6).87 reduce all the complexities that occur in the traditional system of attendance marking (Raman et al, 2016). Developed a CNN-based face recognition system for attendance accuracy purposes.…”

Section: Suggested Citationmentioning

confidence: 99%

“…Development of a face recognition-based automatic student attendance system using CNN Convolutional Neural Networks which includes data entry, dataset training, face recognition, and attendance entry (Raman et al, 2016). The system can detect and recognize multiple people's face from video streams and automatically record daily attendance (Kowsalya et al, 2008).…”

Section: Literature Reviewmentioning

confidence: 99%

Face Recognition Based Attendance System Using Histogram of Oriented Gradients and Linear Support Vector Machine

Sanap,

Narwade,

Goge

et al. 2023

ejtas

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In the 21st century, modern technology is playing an important role in providing innovative on traditional challenges across various domains or sectors. One such challenging task is of daily attendance marking and tracking. Manual attendance requires efforts and it is time-consuming. Sometimes attendance cannot be mark due to human errors. Relying on voice, iris, or fingerprint recognition, increases the complexity and the hardware infrastructure of the system and also increases the cost. To effectively address such issues, we have developed a “Camera based Attendance System”. This system encompasses several crucial stages, including data entry, dataset of multiple people. It is an image-based face recognition system for marking attendance on the SQL database. It excels in detecting and recognizing multiple individuals faces from image and comparing it with the dataset for accurately marking the attendance. This makes the attendance marking process fully automatic. Remarkably, our proposed system attains an impressive recognition and provides the accuracy of approximately 95%. With this solution, daily attendance marking and recording becomes effortless and the stored attendance record can be also used in future if require, eliminating the risk of attendance not getting marked due to human error.

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“…Recent advancement in information and communication technology have paved ways for biomedical signal processing, making it possible to analyses signals from human bodies and study brainwaves. This has led to the development of brainwaves detection and analysis, and has gained tremendous research attention in the recent years [1].…”

Section: Introductionmentioning

confidence: 99%

Brainwave Detection Model for Panic Attacks Based on Event-related Potential

Sirikongtham¹,

Paireekreng²,

Chit³

2019

JUBPAS

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Panic attacks could adversely affect a patient’s daily life and can pose risks to others. The symptoms of panic attacks can be timely observed by detecting the brainwave. This research presents a model that can evaluate the level of panic attack symptoms using the brainwaves detection during (or before) the symptom occurs. It helps monitor the patient’s brainwave based on Event-related potential (ERP). The model is derived from the simulation with horror pictures and frightening sound on the experimental group of 30 people. The survey related to symptoms has been used regarding to the criteria of the Beck Anxiety Inventory (BAI). The results showed that there is a consistent change of Electroencephalography (EEG) in each change of brainwaves where its quantitative analysis found that the changes of Beta, Gamma, and Alpha directly affect the model of Brainwaves Panic Attacks Measurement (BPAM) which is associated with panic attacks. 1 out of 30 cases scored higher than the average of the BPAM at 220 The Model BPAM can detect the risk to be Panic Attack compared to the use of tests Beck Anxiety Inventory (BAI) were found to be consistent. The test value BAI Score 19-63 was BPAM Score 401-1000. In addition, the results found that at P300 the brainwave pattern of EEG in meditation had decreased significantly whereas the brainwave related to attention had increased considerably for which human brain can potentially respond to stimulated external events.

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Design of a robust method to acquire EOG signals using Bio-medical signal processing

Cited by 6 publications

References 4 publications

Vision substitution using audio-tactual sensation

Vision substitution using audio-tactual sensation

Face Recognition Based Attendance System Using Histogram of Oriented Gradients and Linear Support Vector Machine

Brainwave Detection Model for Panic Attacks Based on Event-related Potential

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