@inproceedings{a0e9089d185e48f08398d985a0991a8f,
title = "An Autoencoder-based Approach to Predict Subjective Pain Perception from High-density Evoked EEG Potentials",
abstract = "Pain is a subjective experience and clinicians need to treat patients with accurate pain levels. EEG has emerged as a useful tool for objective pain assessment, but due to the low signal-to-noise ratio of pain-related EEG signals, the prediction accuracy of EEG-based pain prediction models is still unsatisfactory. In this paper, we proposed an autoencoder model based on convolutional neural networks for feature extraction of pain-related EEG signals. More precisely, we used EEGNet to build an autoencoder model to extract a small set of features from high-density pain-evoked EEG potentials and then establish a machine learning models to predict pain levels (high pain vs. low pain) from extracted features. Experimental results show that the new autoencoder-based approach can effectively identify pain-related features and can achieve better classification results than conventional methods.",
keywords = "EEG, autoencoder, deep learning, laser-evoked potentials, pain",
author = "Jiahao Wang and Mengying Wei and Li Zhang and Gan Huang and Zhen Liang and Linling Li and Zhiguo Zhang",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 ; Conference date: 20-07-2020 Through 24-07-2020",
year = "2020",
month = jul,
doi = "10.1109/EMBC44109.2020.9176644",
language = "英语",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1507--1511",
booktitle = "42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society",
address = "美国",
}