Focused State Recognition Using EEG with Eye Movement-Assisted Annotation
This work addresses the challenge of distinguishing focused vs. unfocused states for applications in brain-computer interfaces or cognitive monitoring, but it is incremental as it builds on existing deep learning models for EEG analysis.
The paper tackled the problem of recognizing focused states from EEG and eye movement signals by proposing an annotation method that integrates binocular focusing point disparity with EEG features, achieving 90.16% accuracy in subject-dependent experiments.
With the rapid advancement in machine learning, the recognition and analysis of brain activity based on EEG and eye movement signals have attained a high level of sophistication. Utilizing deep learning models for learning EEG and eye movement features proves effective in classifying brain activities. A focused state indicates intense concentration on a task or thought. Distinguishing focused and unfocused states can be achieved through eye movement behaviors, reflecting variations in brain activities. By calculating binocular focusing point disparity in eye movement signals and integrating relevant EEG features, we propose an annotation method for focused states. The resulting comprehensive dataset, derived from raw data processed through a bio-acquisition device, includes both EEG features and focused labels annotated by eye movements. Extensive training and testing on several deep learning models, particularly the Transformer, yielded a 90.16% accuracy on the subject-dependent experiments. The validity of this approach was demonstrated, with cross-subject experiments, key frequency band and brain region analyses confirming its generalizability and providing physiological explanations.