@inproceedings{982b0cd8dae641c4ba15f53bd1fd0a1e,
title = "EEG-Based Depression Recognition Using Convolutional Neural Network with FFT and EMD",
abstract = "Deep learning methods have been widely adopted in the field of computer-aided EEG diagnosis, one of the major topics to be investigated is the input format of EEG data. Related researches have reported the application of Fast Fourier Transform (FFT) to topology-preserving multi-spectral images generation on three frequency bands (i.e. theta, alpha and beta) jointed to preserve the spatial information. In our work, we proposed a new approach of using Empirical Mode Decomposition (EMD) instead of FFT algorithm to generate topology-preserving multi-spectral images on intrinsic mode functions (IMFs) jointed and only on the single IMF. Meanwhile, images generated on three frequency bands jointed and the single frequency band were also used for comparison. We then applied two convolutional neural network (CNN) structures to distinguish depression patients and normal subjects from the topology-preserving multi-spectral images. As a result, both convolutional networks obtain better accuracies on three IMFs jointed (about 5% better, ≈ 75% vs. ≈ 70%) than three frequency bands jointed. Analysis based on the single frequency band indicates that alpha band performs the best, with an accuracy of more than 78%, and among all classification results, the best classification accuracy obtained is 80.23% on IMF2. The results are encouraging, despite the limited size of our cohort, the use of EMD and our findings cast a new light on application of deep learning method to EEG-based depression recognition.",
keywords = "CNN, Classification, Depression, EEG, EMD, FFT",
author = "Jing Zhu and Xiaowei Li and Pengfei Hou and Bin Hu and Xin Zhang",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 ; Conference date: 05-12-2023 Through 08-12-2023",
year = "2023",
doi = "10.1109/BIBM58861.2023.10385385",
language = "English",
series = "Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2601--2608",
editor = "Xingpeng Jiang and Haiying Wang and Reda Alhajj and Xiaohua Hu and Felix Engel and Mufti Mahmud and Nadia Pisanti and Xuefeng Cui and Hong Song",
booktitle = "Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023",
address = "United States",
}