Learning Spatial-Spectral-Temporal EEG Representations with Deep Attentive-Recurrent-Convolutional Neural Networks for Pain Intensity Assessment-Reference-Cited by-同舟云学术

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Learning Spatial-Spectral-Temporal EEG Representations with Deep Attentive-Recurrent-Convolutional Neural Networks for Pain Intensity Assessment

Published:2022-01 Issue: Volume:481 Page:144-155
ISSN:0306-4522
Container-title:Neuroscience
language:en
Short-container-title:Neuroscience

Author:

Wu Fengjie,Mai Weijian,Tang Yisheng,Liu Qingkun,Chen Jiangtao,Guo Ziqian

Funder

Science and Technology Planning Project of Guangdong Province

National College Students Innovation and Entrepreneurship Training Program

Publisher

Elsevier BV

Subject

General Neuroscience

Reference42 articles.

1. Differentiation of pain levels by deploying various electroencephalogram synchronization features and a dynamic ensemble selection mechanism;Afrasiabi;Physiol Meas,2020

2. A trivariate clough-tocher scheme for tetrahedral data;Alfeld;Comput Aided Geom Des,1984

3. A survey of cross-validation procedures for model selection;Arlot;Statistics Surveys,2010

4. The effect of experimenter gender on autonomic and subjective responses to pain stimuli;Aslaksen;Pain,2007

5. Learning representations from EEG with deep recurrent-convolutional neural networks;Bashivan,2015

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1. A Spatiotemporal Deep Learning Framework for Scalp EEG-Based Automated Pain Assessment in Children;IEEE Transactions on Biomedical Engineering;2024-06

2. Learning spatial-spectral-temporal EEG representations with dual-stream neural networks for motor imagery;Biomedical Signal Processing and Control;2024-06

3. A review of Graph Neural Networks for Electroencephalography data analysis;Neurocomputing;2023-12

4. Prediction of decision-making performance post-longitudinal tDCS administration via EEG features and machine learning;Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments;2023-07-05

5. A Novel End-to-end Network Based on a bidirectional GRU and a Self-Attention Mechanism for Denoising of Electroencephalography Signals;Neuroscience;2022-11

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