  The electrical signal emitted by the eyes movement produces a very strong
artifact on EEG signaldue to its close proximity to the sensors and abundance
of occurrence. In the context of detectingeye blink artifacts in EEG waveforms
for further removal and signal purification, multiple strategieswhere proposed
in the literature. Most commonly applied methods require the use of a large
numberof electrodes, complex equipment for sampling and processing data. The
goal of this work is to createa reliable and user independent algorithm for
detecting and removing eye blink in EEG signals usingCNN (convolutional neural
network). For training and validation, three sets of public EEG data wereused.
All three sets contain samples obtained while the recruited subjects performed
assigned tasksthat included blink voluntarily in specific moments, watch a
video and read an article. The modelused in this study was able to have an
embracing understanding of all the features that distinguish atrivial EEG
signal from a signal contaminated with eye blink artifacts without being
overfitted byspecific features that only occurred in the situations when the
signals were registered.
