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Title: Scalp EEG-based pain detection using convolutional neural network
Authors: Chen, Duo
Zhang, Haihong
Kavitha, Perumpadappil Thomas
Loy, Fong Ling
Ng, Soon Huat
Wang, Chuanchu
Phua, Kok Soon
Tjan, Soon Yin
Yang, Su-Yin
Guan, Cuntai
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Chen, D., Zhang, H., Kavitha, P. T., Loy, F. L., Ng, S. H., Wang, C., Phua, K. S., Tjan, S. Y., Yang, S. & Guan, C. (2022). Scalp EEG-based pain detection using convolutional neural network. IEEE Transactions On Neural Systems and Rehabilitation Engineering, 30, 274-285.
Journal: IEEE Transactions on Neural Systems and Rehabilitation Engineering 
Abstract: Pain is an integrative phenomenon coupled with dynamic interactions between sensory and contextual processes in the brain, often associated with detectable neurophysiological changes. Recent advances in brain activity recording tools and machine learning technologies have intrigued research and development of neurocomputing techniques for objective and neurophysiology-based pain detection. This paper proposes a pain detection framework based on Electroencephalogram (EEG) and deep convolutional neural networks (CNN). The feasibility of CNN is investigated for distinguishing induced pain state from resting state in the recruitment of 10 chronic back pain patients. The experimental study recorded EEG signals in two phases: 1. movement stimulation (MS), where induces back pain by executing predefined movement tasks; 2. video stimulation (VS), where induces back pain perception by watching a set of video clips. A multi-layer CNN classifies the EEG segments during the resting state and the pain state. The novel approach offers high and robust performance and hence is significant in building a powerful pain detection algorithm. The area under the receiver operating characteristic curve (AUC) of our approach is 0.83 ± 0.09 and 0.81 ± 0.15, in MS and VS, respectively, higher than the state-of-the-art approaches. The sub-brain-areas are also analyzed, to examine distinct brain topographies relevant for pain detection. The results indicate that MS-induced pain tends to evoke a generalized brain area, while the evoked area is relatively partial under VS-induced pain. This work may provide a new solution for researchers and clinical practitioners on pain detection.
ISSN: 1534-4320
DOI: 10.1109/TNSRE.2022.3147673
Schools: School of Computer Science and Engineering 
Rights: © The Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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