EEG multipurpose eye blink detector using convolutional neural network

TL;DR

This paper presents a convolutional neural network-based method for reliable, user-independent detection and removal of eye blink artifacts in EEG signals, aiming to improve signal quality with fewer electrodes and simpler equipment.

Contribution

The study introduces a CNN model capable of detecting eye blink artifacts in EEG signals across various scenarios without overfitting, using publicly available datasets.

Findings

High accuracy in detecting eye blinks across different tasks

Effective removal of eye blink artifacts from EEG signals

Model generalizes well to different subjects and conditions

Abstract

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.