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.

中文翻译：

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基于互信息的融合模型 (MIBFM)：使用脑电图和瞳孔区域信号识别轻度抑郁症

轻度抑郁症的检测有利于抑郁症的早期干预和治疗。本研究探索脑电图（EEG）和瞳孔区域信号的融合，以建立有效且方便的轻度抑郁症识别模型。我们提出了基于互信息的融合模型（MIBFM），创新性地利用瞳孔区域信号根据互信息选择脑电图电极。然后，我们从不同频带的脑电图和瞳孔区域信号中提取特征，并使用去噪自动编码器融合双峰特征。实验结果表明，MIBFM可以获得最高87.03%的准确率。MIBFM 表现出比其他现有方法更好的性能。我们的研究结果验证了使用瞳孔区域作为信号的有效性，这使得使用高分辨率相机可以轻松获得眼动信号，并且基于互信息的脑电电极选择方案也被证明是数据降维和多模态互补信息筛选的适用解决方案。该研究为利用脑电图和瞳孔区域信号的多模态数据识别轻度抑郁症提供了新的思路，并为便携式和通用应用系统的开发提供了理论基础。