Focused State Recognition Using EEG with Eye Movement-Assisted Annotation

TL;DR

This paper presents a novel method combining EEG and eye movement data to accurately recognize focused mental states using deep learning, achieving over 90% accuracy and demonstrating generalizability across subjects.

Contribution

It introduces an annotation approach leveraging eye movement features to label focused states and creates a comprehensive dataset for training deep learning models.

Findings

Transformer model achieved 90.16% accuracy in focused state recognition

Eye movement features effectively distinguish focused from unfocused states

The approach generalizes well across different subjects

Abstract

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.