Understanding and characterizing blinks can contribute to developing applications in areas such as human-computer interaction, fatigue monitoring, and medical diagnosis. This work involved the collection and analysis of eye-tracking data from 44 subjects to monitor voluntary and involuntary blinks. Using eye openness metrics, our work builds on previous research that uses the same metric to monitor blinks. Blink duration thresholds were obtained from past studies involving eye images and were categorized as voluntary, involuntary, or invalid. In addition, a neural network model was developed and trained to classify blinks as voluntary or involuntary. The model achieved 92% accuracy on the test set, demonstrating the effectiveness of using eye openness values for this classification task. This open-access dataset we created and the model can provide a solid foundation for further research and applications in automated blink detection and analysis, and the potential creation of interactive systems that take voluntary blinks as input.