Fusing of Electroencephalogram and Eye Movement With Group Sparse Canonical Correlation Analysis for Anxiety Detection

Electroencephalogram (EEG) has been widely used for the detection of anxiety because of its ability to reflect the functional activities of the brain. However, EEG alone may not provide precision in the detection of anxiety because other emotional disorders usually trigger the same changes in brain function. To discover effective diagnostic indicators and to achieve more precise anxiety detection, we integrate eye movement information into EEG and divide the features into groups according to their respective characteristics. Then, we use group sparse canonical correlation analysis (GSCCA) to investigate group structure information among EEG and eye movement features and obtain an effective fusion representation of EEG and eye movement to achieve more precise detection of anxiety mood. The experimental results from 45 anxious subjects and 47 normal controls from the Healthy Brain Network (HBN) dataset showed that GSCCA could be effectively used to explore the correlation between EEG features within different scalp regions and eye movement features from several aspects. Visual behaviors, including saccades and fixation, are more linearly related to the power spectrum of EEG on the scalp area corresponding to the visual region of the brain. The ultimate fusion representation achieved an optimal classification accuracy of 82.70 percent with the support vector machine (SVM) classifier on the gamma band of EEG.