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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2016 Vol.6(3): 191-195 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2016.6.3.597

Extracting Blink Rate Variability from EEG Signals

Temesgen Gebrehiwot, Rafal Paprocki, Marija Gradinscak, Artem Lenskiy
Abstract—Generally, blinks are treated on equal with artifacts and noise while analyzing EEG signals. However, blinks carry important information about mental processes and thus it is important to detect blinks accurately. The aim of the presented study is to propose a blink detection method and discuss its application for extracting blink rate variability, a novel concept that might shed some light on the mental processes. In this study, 14 EEG recordings were selected for assessing the quality of the proposed blink detection algorithm.

Index Terms—Blink rate variability, inter blink interval dynamics, EEG artifacts.

Artem Lenskiy is with Korea University of Technology and Education 1600, Chungjeol-ro, Byeongcheon-myeon, Dongnam-gu, Cheonan-si, Chungcheongnam-do 31253, Republic of Korea (e-mail: lensky@koreatech.ac.kr).


Cite: Temesgen Gebrehiwot, Rafal Paprocki, Marija Gradinscak, Artem Lenskiy, "Extracting Blink Rate Variability from EEG Signals," International Journal of Machine Learning and Computing vol. 6, no. 3, pp. 191-195, 2016.

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