Prediction of Human-Computer Interaction Intention Based on Eye Movement and Electroencephalograph Characteristics - Qu 2022

A method for predicting human-computer interaction intentions based on EEG and eye movement signals is proposed to smooth and improve human-computer interaction in the information age. Previous strategies used human-computer interaction data and a single physiological marker. This approach predicts interaction intention using eye movements and EEG data. This approach is evaluated with varied HCI intentions and operator cognitive states. The experimental findings illustrate this method's advantages over others. Experiment 1 used fixation point abscissa Position X (PX), fixation point ordinate Position Y (PY), and saccade amplitude (SA) to determine interaction intention. In experiment 2, pupil diameter, pupil size (PS), and fixed time, fixed time (FD) of eye movement signals cannot accurately predict the operator's cognitive state, thus EEG signals are added. Combining EEG measures R/ with pupil diameter and fixation time accurately identified the cognitive state with 91.67 percent accuracy. Eye movement and EEG features can predict the operator's interaction intention and cognitive state.

Qu, J., Guo, H., Wang, W., & Dang, S. (2022). Prediction of Human-Computer Interaction Intention Based on Eye Movement and Electroencephalograph Characteristics. Frontiers in Psychology, 13