Mutual Information Based Fusion Model (MIBFM): Mild Depression Recognition Using EEG and Pupil Area Signals

Abstract

The detection of mild depression is conducive to the early intervention and treatment of depression. This study explored the fusion of electroencephalography (EEG) and pupil area signals to build an effective and convenient mild depression recognition model. We proposed Mutual Information Based Fusion Model (MIBFM), which innovatively used pupil area signals to select EEG electrodes based on mutual information. Then we extracted features from EEG and pupil area signals in different bands, and fused bimodal features using the denoising autoencoder. Experimental results showed that MIBFM could obtain the highest accuracy of 87.03%. And MIBFM exhibited better performance than other existing methods. Our findings validate the effectiveness of the use of pupil area as signals, which makes eye movement signals can be easily obtained using high resolution camera, and the EEG electrode selection scheme based on mutual information is also proved to be an applicable solution for data dimension reduction and multimodal complementary information screening. This study casts a new light for mild depression recognition using multimodal data of EEG and pupil area signals, and provides a theoretical basis for the development of portable and universal application systems.