EEG-Eye Blink Detection System for Brain Computer Interface

2013 ◽  
pp. 603-608 ◽  
Author(s):  
S. Rihana ◽  
P. Damien ◽  
T. Moujaess
Keyword(s):  
Detection System ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eye Blink ◽  
Blink Detection

scholarly journals Wheelchair controlled by human brainwave using brain-computer interface system for paralyzed patient

2021 ◽  
Vol 10 (6) ◽  
pp. 3032-3041
Author(s):  
Norasyimah Sahat ◽  
Afishah Alias ◽  
Fouziah Md Yassin
Keyword(s):  
Brain Computer Interface ◽  
Threshold Value ◽  
Computer Interface ◽  
Male Adult ◽  
Eye Blink ◽  
Human Attention ◽  
Blink Detection ◽  
Paralyzed Patient ◽  
The Right ◽  
Bci System

Integrated wheelchair controlled by human brainwave using a brain-computer interface (BCI) system was designed to help disabled people. The invention aims to improve the development of integrated wheelchair using a BCI system, depending on the ability individual brain attention level. An electroencephalography (EEG) device called mindwave mobile plus (MW+) has been employed to obtain the attention value for wheelchair movement, eye blink to change the mode of the wheelchair to move forward (F), to the right (R), backward (B) and to the left (L). Stop mode (S) is selected when doing eyebrow movement as the signal quality value of 26 or 51 is produced. The development of the wheelchair controlled by human brainwave using a BCI system for helping a paralyzed patient shows the efficiency of the brainwave integrated wheelchair and improved using human attention value, eye blink detection and eyebrow movement. Also, analysis of the human attention value in different gender and age category also have been done to improve the accuracy of the brainwave integrated wheelchair. The threshold value for male children is 60, male teenager (70), male adult (40) while for female children is 50, female teenager (50) and female adult (30).

A low cost Human Computer Interface for Disabled People based on Eye Blink detection using Brain Signal

2019 ◽  
Author(s):  
Lamiya Rahman ◽  
Jannatul Adan ◽  
Quazi Mutasim Billah ◽  
Md Kamrul Islam ◽  
A.H.M Mostafa Kamal ◽  
...  
Keyword(s):  
Low Cost ◽  
Computer Interface ◽  
Human Computer Interface ◽  
Disabled People ◽  
Eye Blink ◽  
Blink Detection

DEVELOPMENT OF A REAL-TIME SINGLE CHANNEL BRAIN–COMPUTER INTERFACE SYSTEM FOR DETECTION OF DROWSINESS

2017 ◽  
Vol 29 (03) ◽  
pp. 1750019 ◽  
Author(s):  
Malhar Pathak ◽  
A. K. Jayanthy
Keyword(s):  
Real Time ◽  
Single Channel ◽  
Detection System ◽  
Brain Computer Interface ◽  
Reference Electrode ◽  
Computer Interface ◽  
Processing Unit ◽  
Eeg Signal ◽  
Drowsiness Detection ◽  
Interface System

Drowsiness or fatigue condition refers to feeling abnormally sleepy at an inappropriate time, especially during day time. It reduces the level of concentration and slowdown the response time, which eventually increases the error rate while doing any day-to-day activity. It can be dangerous for some people who require higher concentration level while doing their work. Study shows that 25–30% of road accidents occur due to drowsy driving. There are number of methods available for the detection of drowsiness out of which most of the methods provide an indirect measurement of drowsiness whereas electroencephalography provides the most reliable and direct measurement of the level of consciousness of the subject. The aim of this paper is to design and develop a portable and low cost brain–computer interface system for detection of drowsiness. In this study, we are using three dry electrodes out of which two active electrodes are placed on the forehead whereas the reference electrode is placed on the earlobe to acquire electroencephalogram (EEG) signal. Previous research shows that, there is a measurable change in the amplitude of theta ([Formula: see text]) wave and alpha ([Formula: see text]) wave between the active state and the drowsy state and based on this fact theta ([Formula: see text]) wave and alpha ([Formula: see text]) wave are separated from the normal EEG signal. The signal processing unit is interfaced with the microcontroller unit which is programmed to analyze the drowsiness based on the change in the amplitude of theta ([Formula: see text]) wave. An alarm will be activated once drowsiness is detected. The experiment was conducted on 20 subjects and EEG data were recorded to develop our drowsiness detection system. Experimental results have proved that our system has achieved real-time drowsiness detection with an accuracy of approximately 85%.

scholarly journals Eye Blink Detection System for Paralyzed Patients

2020 ◽  
Vol 8 (6) ◽  
pp. 333-336
Keyword(s):  
Motor Neurons ◽  
Detection System ◽  
Brain Wave ◽  
Eye Blink ◽  
Video Oculography ◽  
Incurable Disease ◽  
Blink Detection ◽  
Wave Technique ◽  
Paralyzed Patient ◽  
Visual Technologies

Paralysis of an human being is caused due to the degeneration of motor neurons which weakens the muscle, so that it does not allow patient to move, speak, breathe , and loss in the voluntary actions. It is an incurable disease. To understand the feelings of a paralyzed patients Brain wave technique and Electro-oculography techniques were used. These techniques are afflictive, discomfortable and leads to unconsciousness of the paralyzed patient. The real time video oculography system fills the communication gap between the patient and the world. Video Oculography (VOG) is video-based method of measuring the vertical, torsional and horizontal position components of both the eye blinks with the help of small cameras placed in the head-mounted mask .This paper presents different visual technologies, such as eye blink detection, eye center localization and conversion of the eye blink to speech. The video oculography could achieve accuracy of 0.968.

Identification of Eye Blink Artifacts Using Wireless EEG Headset for Brain Computer Interface System

2018 ◽  
Author(s):  
S. Sridhar ◽  
U. Ramachandraiah ◽  
E. Sathish ◽  
G. Muthukumaran ◽  
P. Rajendra Prasad
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Eye Blink ◽  
Interface System ◽  
Wireless Eeg

Eye-Blink Detection System for Virtual Keyboard

2021 ◽  
Author(s):  
Afraa Z. Attiah ◽  
Enas F. Khairullah
Keyword(s):  
Detection System ◽  
Virtual Keyboard ◽  
Eye Blink ◽  
Blink Detection
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