EEG multipurpose eye blink detector using convolutional neural network
This work addresses the need for simpler, more accessible eye blink detection in EEG analysis, though it appears incremental as it applies an existing CNN method to this domain-specific task.
The paper tackled the problem of detecting and removing eye blink artifacts in EEG signals by developing a convolutional neural network (CNN) algorithm, achieving a reliable and user-independent solution without specifying concrete performance numbers.
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.