Detection of EEG-Based Eye-Blinks Using A Thresholding Algorithm

2021 ◽  
Vol 6 (4) ◽  
pp. 6-12
Author(s):  
Dang-Khoa Tran ◽  
Thanh-Hai Nguyen ◽  
Thanh-Nghia Nguyen
Keyword(s):  
Detection Algorithm ◽  
Time Frequency ◽  
Eye Blink ◽  
Eye Blinks ◽  
User Training ◽  
Electrical Potentials ◽  
Ocular Artifact ◽  
Blink Detection ◽  
Thresholding Algorithm ◽  
Frequency Properties

In the electroencephalography (EEG) study, eye blinks are a commonly known type of ocular artifact that appears most frequently in any EEG measurement. The artifact can be seen as spiking electrical potentials in which their time-frequency properties are varied across individuals. Their presence can negatively impact various medical or scientific research or be helpful when applying to brain-computer interface applications. Hence, detecting eye-blink signals is beneficial for determining the correlation between the human brain and eye movement in this paper. The paper presents a simple, fast, and automated eye-blink detection algorithm that did not require user training before algorithm execution. EEG signals were smoothed and filtered before eye-blink detection. We conducted experiments with ten volunteers and collected three different eye-blink datasets over three trials using Emotiv EPOC+ headset. The proposed method performed consistently and successfully detected spiking activities of eye blinks with a mean accuracy of over 96%.

scholarly journals AUTOMATIC EYE TEMPLATE GENERATION AND TRACKING

2014 ◽  
pp. 226-230
Author(s):  
RICHA MEHTA ◽  
MANISH SHRIVASTAVA
Keyword(s):  
Eye Tracking ◽  
Real Time ◽  
Facial Feature ◽  
Detection Algorithm ◽  
Eye Blink ◽  
Eye Blinks ◽  
The Road ◽  
Blink Detection ◽  
Two Phases ◽  
Real World Problems

Eye blinking is a physiological necessity for humans. This method automatically locates the user’s eye by detecting eye blinks. A system is the improvement of driver carefulness and accident reduction. The driver’s face is tracked while he is driving and he is warned if there seems to be an alerting fact that can result in an accident such as sleepy eyes, or looking out of the road. Furthermore, with a facial feature tracker, it becomes possible to play a synthesized avatar so that it imitates the expressions of the performer. For a user who is incapable of using her hands, a facial expression controller may be a solution to send limited commands to a computer. Eye blinking is one of the prominent areas to solve many real world problems. The process of blink detection consists of two phases. These are eye tracking followed by detection of blink. The work that has been carried out for eye tracking only is not suitable for eye blink detection. Therefore some approaches had been proposed for eye tracking along with eyes blink detection. In this thesis, real time implementation is done to count number of eye blinks in an image sequence. At last after analyzing all these approaches some of the parameters we obtained on which better performance of eye blink detection algorithm depend. This project focuses on automatic eye blink detection in real time. The aim of this thesis is to count the number of eye blinks in a video. This project will be performed on a video database of the facial expressions.

Brain Computing Interface based Auto Calling Using Eye Blink Detection Algorithm

2021 ◽  
Author(s):  
Sree Haran A ◽  
Siyam Adit G ◽  
Vignesh N ◽  
Vimal Athitha S G ◽  
Subash Sakthivel S ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Eye Blink ◽  
Blink Detection

On the wavelet time-frequency analysis algorithm in identification of a cracked rotor

2002 ◽  
Vol 37 (3) ◽  
pp. 239-246 ◽  
Author(s):  
J Zou ◽  
J Chen ◽  
Y P Pu ◽  
P Zhong
Keyword(s):  
Numerical Simulation ◽  
Detection Algorithm ◽  
Sampling Frequency ◽  
Crack Model ◽  
Cracked Rotor ◽  
Simulation Research ◽  
Time Frequency ◽  
Local Flexibility ◽  
The Difference ◽  
Frequency Properties

Based on the hinge crack model and the local flexibility theorem, the local flexibility of a cracked rotor due to the crack and the modified function of the opening and closing of the crack are given; the corresponding dynamic equation of the cracked rotor is modelled; the numerical simulation solutions of the cracked rotor and the uncracked rotor are obtained. By the continuous wavelet time—frequency transform, the wavelet time-frequency properties of the uncracked rotor and the cracked rotor are discussed; the difference between the wavelet time-frequency properties of the cracked rotor and those of the uncracked rotor is presented. A new detection algorithm that uses the wavelet time-frequency transform to identify the crack is proposed. The influence of the sampling frequency on the accuracy and validity of the wavelet time-frequency transform is analysed by numerical simulation research; the preferred sampling frequency is suggested. The experiments on the cracked rotor and the uncracked rotor demonstrate the validity and availability of the algorithm in the identification of the cracked rotor in engineering practices.

Camera-Based Eye Blink Detection Algorithm for Assessing Driver Drowsiness

2019 ◽  
Author(s):  
Mohamed Hedi Baccour ◽  
Frauke Driewer ◽  
Enkelejda Kasneci ◽  
Wolfgang Rosenstiel
Keyword(s):  
Detection Algorithm ◽  
Eye Blink ◽  
Driver Drowsiness ◽  
Blink Detection

Staring Down Death

Crisis ◽  
2016 ◽  
Vol 37 (3) ◽  
pp. 212-217 ◽  
Author(s):  
Thomas E. Joiner ◽  
Melanie A. Hom ◽  
Megan L. Rogers ◽  
Carol Chu ◽  
Ian H. Stanley ◽  
...  
Keyword(s):  
Suicide Risk ◽  
Clinical Utility ◽  
Blink Rate ◽  
Specific Task ◽  
Careful Planning ◽  
Eye Blink ◽  
Eye Blinks ◽  
Potential Clinical Utility ◽  
The Relationship

Abstract. Background: Lowered eye blink rate may be a clinically useful indicator of acute, imminent, and severe suicide risk. Diminished eye blink rates are often seen among individuals engaged in heightened concentration on a specific task that requires careful planning and attention. Indeed, overcoming one’s biological instinct for survival through suicide necessitates premeditation and concentration; thus, a diminished eye blink rate may signal imminent suicidality. Aims: This article aims to spur research and clinical inquiry into the role of eye blinks as an indicator of acute suicide risk. Method: Literature relevant to the potential connection between eye blink rate and suicidality was reviewed and synthesized. Results: Anecdotal, cognitive, neurological, and conceptual support for the relationship between decreased blink rate and suicide risk is outlined. Conclusion: Given that eye blinks are a highly observable behavior, the potential clinical utility of using eye blink rate as a marker of suicide risk is immense. Research is warranted to explore the association between eye blink rate and acute suicide risk.

Design and Implementation of Artificial Human Hand Model Based on Eye Blink Detection

2020 ◽  
Author(s):  
K Prasanthi Jasmine ◽  
SK Mahaboob Subhani ◽  
V Ajith ◽  
CH Rakesh Kumar
Keyword(s):  
Human Hand ◽  
Eye Blink ◽  
Design And Implementation ◽  
Model Based ◽  
Hand Model ◽  
Blink Detection ◽  
Human Hand Model

scholarly journals Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3524
Author(s):  
Rongru Wan ◽  
Yanqi Huang ◽  
Xiaomei Wu
Keyword(s):  
Ventricular Fibrillation ◽  
Cross Validation ◽  
Rhythmic Activity ◽  
Detection Algorithm ◽  
Motion Artifacts ◽  
Cardiac Monitoring ◽  
Mechanical Activity ◽  
Time Frequency ◽  
S Transform ◽  
Fatal Arrhythmia

Ventricular fibrillation (VF) is a type of fatal arrhythmia that can cause sudden death within minutes. The study of a VF detection algorithm has important clinical significance. This study aimed to develop an algorithm for the automatic detection of VF based on the acquisition of cardiac mechanical activity-related signals, namely ballistocardiography (BCG), by non-contact sensors. BCG signals, including VF, sinus rhythm, and motion artifacts, were collected through electric defibrillation experiments in pigs. Through autocorrelation and S transform, the time-frequency graph with obvious information of cardiac rhythmic activity was obtained, and a feature set of 13 elements was constructed for each 7 s segment after statistical analysis and hierarchical clustering. Then, the random forest classifier was used to classify VF and non-VF, and two paradigms of intra-patient and inter-patient were used to evaluate the performance. The results showed that the sensitivity and specificity were 0.965 and 0.958 under 10-fold cross-validation, and they were 0.947 and 0.946 under leave-one-subject-out cross-validation. In conclusion, the proposed algorithm combining feature extraction and machine learning can effectively detect VF in BCG, laying a foundation for the development of long-term self-cardiac monitoring at home and a VF real-time detection and alarm system.

scholarly journals Mask-Type Sensor for Pulse Wave and Respiration Measurements and Eye Blink Detection

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4895
Author(s):  
Thanh-Vinh Nguyen ◽  
Masaaki Ichiki
Keyword(s):  
Pulse Wave ◽  
Pressure Change ◽  
Frequency Component ◽  
High Frequency Component ◽  
Sensing Element ◽  
Eye Blink ◽  
Mask Type ◽  
Blink Detection ◽  
The Subject ◽  
Air Bag

This paper reports on a mask-type sensor for simultaneous pulse wave and respiration measurements and eye blink detection that uses only one sensing element. In the proposed sensor, a flexible air bag-shaped chamber whose inner pressure change can be measured by a microelectromechanical system-based piezoresistive cantilever was used as the sensing element. The air bag-shaped chamber is fabricated by wrapping a sponge pad with plastic film and polyimide tape. The polyimide tape has a hole to which the substrate with the piezoresistive cantilever adheres. By attaching the sensor device to a mask where it contacts the nose of the subject, the sensor can detect the pulses and eye blinks of the subject by detecting the vibration and displacement of the nose skin caused by these physiological parameters. Moreover, the respiration of the subject causes pressure changes in the space between the mask and the face of the subject as well as slight vibrations of the mask. Therefore, information about the respiration of the subject can be extracted from the sensor signal using either the low-frequency component (<1 Hz) or the high-frequency component (>100 Hz). This paper describes the sensor fabrication and provides demonstrations of the pulse wave and respiration measurements as well as eye blink detection using the fabricated sensor.

Embedded System for Eye Blink Detection Using Machine Learning Technique

2021 ◽  
Author(s):  
Bishar R. Ibrahim ◽  
Farhad M. Khalifa ◽  
Subhi R. M. Zeebaree ◽  
Nashwan A. Othman ◽  
Ahmed Alkhayyat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Embedded System ◽  
Machine Learning Technique ◽  
Eye Blink ◽  
Learning Technique ◽  
Blink Detection
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