Wavelet Design for Automatic Real-Time Eye Blink Detection and Recognition in EEG Signals

  • Michael Gabriel Miranda Department of Informatic Engineering Metropolitan University of Technology, Chile. Jose Pedro Alessandri 1242, ~Nu~noa, Santiago, Chile
  • Renato Alberto Salinas Department of Mechanical Engineering University of Santiago, Chile Av. Libertador Bernardo O'Higgins 3363, Santiago, Chile.
  • Ulrich Raff Department of Physics University of Santiago, Chile Av. Libertador Bernardo O'Higgins 3363, Santiago, Chile.
  • Oscar Magna Department of Informatic Engineering Metropolitan University of Technology, Chile Jose Pedro Alessandri 1242, ~Nu~noa, Santiago, Chile


The blinking of an eye can be detected in electroencephalographic (EEG) recordings and can be understood as a useful control signal in some information processing tasks. The detection of a specific pattern associated with the blinking of an eye in real time using EEG signals of a single channel has been analyzed. This study considers both theoretical and practical principles enabling the design and implementation of a system capable of precise real-time detection of eye blinks within the EEG signal. This signal or pattern is subject to considerable scale changes and multiple incidences. In our proposed approach, a new wavelet was designed to improve the detection and localization of the eye blinking signal. The detection of multiple occurrences of the blinking perturbation in the recordings performed in real-time operation is achieved with a window giving a time-limited projection of an ongoing analysis of the sampled EEG signal.


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How to Cite
MIRANDA, Michael Gabriel et al. Wavelet Design for Automatic Real-Time Eye Blink Detection and Recognition in EEG Signals. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 14, n. 3, p. 375-387, may 2019. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/3516>. Date accessed: 12 july 2020. doi: https://doi.org/10.15837/ijccc.2019.3.3516.


Biological signals, electroencephalogram, brain computer interface, eye blink detection, pattern recognition, wavelet design