A CNN-based approach for detecting eye blink episodes in EEG signals

Objective.This study aims to develop and evaluate a convolutional neural network (CNN)-based architecture for detecting eye blink episodes in electroencephalographic (EEG) signals, with a focus on the precise detection of individual events rather than their classification into predefined categories.Approach.The proposed method integrates a CNN-based architecture with a dedicated data augmentation technique that can capture the characteristic time patterns of the blink episodes.Main results.The performance of the proposed approach was validated using EEG data collected from 10 subjects across three experimental setups. The average detection rates reached 96.91% and 97.18% for individual subject tests, and 94.45% for cross-subject evaluation.Significance.The results demonstrate the high effectiveness and strong generalization capabilities of the proposed method, emphasizing its potential applications in improving neural data quality, cognitive state monitoring, and assistive technologies.