  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.
