EEG Multipurpose Eye Blink Detector using convolutional neural network

Amanda Ferrari Iaquinta,Ana Carolina De Sousa Silva,Gustavo Voltani Von Atzingen,Jessica Monique De Toledo,Aldrumont Ferraz Júnior

doi:10.33448/rsd-v10i15.22712

Amanda Ferrari Iaquinta, Ana Carolina De Sousa Silva + Show 3 more

Open Access

https://doi.org/10.33448/rsd-v10i15.22712

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Abstract

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

Highlights

Electroencephalography had its origin by the 1920s, Hans Berger, a neuropsychiatrist from Germany, recorded potentials from the scalp of patients with skull defects and, a few years later, with more sensitive equipment from intact subjects
A brain-computer interface (BCI) is a device that connects the brain to a computer and decodes in real time a specific, predefined brain activity
The raw EEG data present in the datasets used to train and evaluate this model was collected by Agarwal and Sivakumar (Agarwal, 2019) in a series of experiments described in the article “Blink: A Fully Automated Unsupervised Algorithm for Eye Blink Detection in EEG Signals", that was a laboratory research study

Summary

Introduction

Electroencephalography had its origin by the 1920s, Hans Berger, a neuropsychiatrist from Germany, recorded potentials from the scalp of patients with skull defects and, a few years later, with more sensitive equipment from intact subjects. Today the field has moved from simple and artefact-sensitive EEG recording to making real the vision of brain-computer communication. A brain-computer interface (BCI) is a device that connects the brain to a computer and decodes in real time a specific, predefined brain activity. This brain activity has to be measured either directly, via the electrical activity of nerve cells, or indirectly (Kübler, 2019)

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