TY  - JOUR
T1  - EEG Signal Processing Model for Eye Blink Detection
AU - Guun Yoo, Sang AU - Cerezo Ramírez, Johanna AU - Murillo López, José Luis AU - Ortíz Chicaiza, Kelvin AU - Poveda Zavala, Sebastián 
JO  - Journal of Engineering and Applied Sciences
VL  - 15
IS  - 7
SP  - 1671
EP  - 1675
PY  - 2020
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2020.1671.1675
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2020.1671.1675
KW  - eyelids
KW  -signal analysis
KW  -OpenBCI
KW  -digital signal processing
KW  -eye blink detection
KW  -EEG
KW  -electroencephalography
KW  -featureextraction
AB  - Electroencephalography devices such as the OpenBCI Cyton Biosensing board create a noninvasive
and inexpensive way of acquiring signals generated by the brain. These signals are influenced by different types
of brain stimuli such as eye blinks but they are also includes a large amount of noise, e.g., generated by the
board. However, the noise can be removed with the help of proven filters. In this aspect, the intention of this
work is to demonstrate how using different type of filters, it is possible to clean the noise from the brain signals
acquired using an encephalography devices (such as Cytonbiosensing board) which are generated when a user
blinks his/her eyes and classify them in different type of blinks. We have chosen the study of eye blink brain
signals, since, they present a wide range of real-life applications. Our model includes a simple algorithm that
classifies user-generated eye blinks into short intended blinks and long composed blinks. Experimental results
of the proposed model show an accuracy of 96% which enables the development of real-life applications that
do not require real-time control such as IoT devices.
ER  -