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A CNN-based approach for detecting eye blink episodes in EEG signals
1West Pomeranian University of Technology in Szczecin, Żołnierska 49, 71-210 Szczecin, Poland.
Journal of Neural Engineering
|May 6, 2025
Summary
This study introduces a novel convolutional neural network (CNN) for precise electroencephalographic (EEG) eye blink detection. The method achieves high accuracy in identifying individual blink events, enhancing neural data analysis.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Machine Learning
Background:
- Electroencephalography (EEG) is crucial for brain activity monitoring.
- Accurate detection of artifacts like eye blinks is essential for reliable EEG analysis.
- Existing methods may struggle with precise individual event detection.
Purpose of the Study:
- To develop a convolutional neural network (CNN)-based architecture for detecting individual eye blink episodes in EEG signals.
- To focus on precise event detection rather than classification into predefined categories.
- To evaluate the performance and generalization capabilities of the proposed CNN architecture.
Main Methods:
- Integration of a CNN architecture with a specialized data augmentation technique.
- The data augmentation is designed to capture the temporal patterns characteristic of eye blinks.
- Validation using EEG data from 10 subjects across three experimental setups.
Main Results:
- Achieved average detection rates of 96.91% and 97.18% in individual subject tests.
- Demonstrated a cross-subject evaluation accuracy of 94.45%.
- The proposed method shows high effectiveness and strong generalization capabilities.
Conclusions:
- The developed CNN-based method offers a highly effective approach for precise eye blink detection in EEG.
- The approach exhibits strong generalization, indicating its robustness across different subjects and conditions.
- Potential applications include improving neural data quality, cognitive state monitoring, and developing assistive technologies.
