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

Abstract

<i>Objective.</i>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.<i>Approach.</i>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.<i>Main results.</i>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.<i>Significance.</i>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.