基于多源域自适应的跨被试情感脑电识别

高寒冰; 张驰; 靳明艳; 肖杨; 丛丰裕

doi:10.11959/j.issn.2096-6652.202106

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基于多源域自适应的跨被试情感脑电识别

专题：情感脑机接口 | 更新时间：2024-06-05

- 基于多源域自适应的跨被试情感脑电识别
- Cross-subject emotional EEG recognition based on multi-source domain adaptation
- 智能科学与技术学报 2021年3卷第1期页码：59-64
- 作者机构：
  
  1. 大连理工大学电子信息与电气工程学部生物医学工程学院，辽宁大连 116024
  2. 大连理工大学电子信息与电气工程学部人工智能学院，辽宁大连 116024
- 作者简介：
  
  [ "高寒冰（1996- ），女，大连理工大学电子信息与电气工程学部生物医学工程学院硕士生，主要研究方向为脑电和视频分析与处理。" ]
  [ "张驰（1987- ），男，博士，大连理工大学电子信息与电气工程学部生物医学工程学院讲师，主要研究方向为脑机接口和生物电信号处理。" ]
  [ "靳明艳（1998- ），女，大连理工大学电子信息与电气工程学部生物医学工程学院硕士生，主要研究方向为脑电信号处理。" ]
  [ "肖杨（1997- ），女，大连理工大学电子信息与电气工程学部生物医学工程学院硕士生，主要研究脑电的分析与处理。" ]
  [ "丛丰裕（1978- ），男，博士，大连理工大学电子信息与电气工程学部教授、博士生导师，生物医学工程学院和人工智能学院双聘教授，生物医学工程一级学科博士点负责人，人工智能学院脑科学与类脑智能方向责任教授。中国生物医学工程学会青年工作委员会副主任委员，辽宁省神经科学学会副会长，主要研究方向为脑科学、医学和计算机科学等高度交叉的认知神经科学方法学。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61703069);国家重点基础研究发展计划资金资助项目;The National Key Basic Research Program of China(JCKY2019110B009)
- DOI：10.11959/j.issn.2096-6652.202106
  中图分类号： R318
- 网络出版日期：2021-03，
  
  纸质出版日期：2021-03-15
- 稿件说明：
移动端阅览
高寒冰, 张驰, 靳明艳, 等. 基于多源域自适应的跨被试情感脑电识别[J]. 智能科学与技术学报, 2021,3(1):59-64.

Hanbing GAO, Chi ZHANG, Mingyan JIN, et al. Cross-subject emotional EEG recognition based on multi-source domain adaptation[J]. Chinese journal of intelligent science and technology, 2021, 3(1): 59-64.
高寒冰, 张驰, 靳明艳, 等. 基于多源域自适应的跨被试情感脑电识别[J]. 智能科学与技术学报, 2021,3(1):59-64. DOI： 10.11959/j.issn.2096-6652.202106.

Hanbing GAO, Chi ZHANG, Mingyan JIN, et al. Cross-subject emotional EEG recognition based on multi-source domain adaptation[J]. Chinese journal of intelligent science and technology, 2021, 3(1): 59-64. DOI： 10.11959/j.issn.2096-6652.202106.

摘要

在基于脑电信号的情感检测过程中，受被试间脑电信号的差异影响，传统的机器学习和深度学习方法不能很好地针对脑电数据建立通用的分类检测模型。针对此问题，将每个被试看作独立的域，建立针对脑电数据的多源域自适应的跨被试情感脑电识别模型，并在公开数据集上进行验证。验证结果表明，与单源域模型相比，提出的模型有更好的跨被试特征提取及分类能力。

Abstract

During the recognition of emotion based on electroencephalography (EEG) signals

traditional machine learning and deep learning methods cannot establish a general classification and detection model for EEG data due to the differences in EEG signals among subjects.Each subject was treated as an independent domain

a multi-source cross-subject emotional EEG recognition model was established

and the model was verified on the public dataset.The evaluation results show that compared with single-source domain model

the proposed model has better cross-subject feature extraction and classification capabilities.

关键词

Keywords

references

WIDGE A S , DOUGHERTY D D , MORITZ C T . Affective braincomputer interfaces as enabling technology for responsive psychiatric stimulation [J ] . Brain-Computer Interfaces , 2014 , 1 ( 2 ): 126 - 136 .

ALGHOWINEM S , GOECKE R , WAGNER M , et al . Multimodal depression detection:fusion analysis of paralinguistic,head pose and eye gaze behaviors [J ] . IEEE Transactions on Affective Computing , 2018 , 9 ( 4 ): 478 - 490 .

CAPONETTI L , BUSCICCHIO C A , CASTELLANO G . Biologically inspired emotion recognition from speech [J ] . EURASIP Journal on Advances in Signal Processing , 2011 ( 1 ): 1 - 10 .

ARRIAGA O , VALDENEGRO-TORO M , PLOGER P . Real-time convolutional neural networks for emotion and gender classification [J ] . arXiv preprint , 2017 ,arXiv:1710.07557.

LAN Z R , SOURINA O , WANG L P , et al . Domain adaptation techniques for EEG-based emotion recognition:a comparative study on two public datasets [J ] . IEEE Transactions on Cognitive and Developmental Systems , 2019 , 11 ( 1 ): 85 - 94 .

娄晓光 , 陈兰岚 , 宋振振 . 基于多源域迁移学习的脑电情感识别 [J ] . 计算机工程与设计 , 2020 , 41 ( 7 ): 2011 - 2018 .

LOU X G , CHEN L L , SONG Z Z . EEG emotion recognition based on multi-source domain transfer learning [J ] . Computer Engineering and Design , 2020 , 41 ( 7 ): 2011 - 2018 .

PAN S J , YANG Q . A survey on transfer learning [J ] . IEEE Transactions on Knowledge and Data Engineering , 2010 , 22 ( 10 ): 1345 - 1359 .

GANIN Y , LEMPITSKY V . Unsupervised domain adaptation by backpropagation [C ] // Proceedings of the 32nd International Conference on Machine Learning .[S.l.:s.n. ] , 2015 : 325 - 333 .

GUI L , XU R F , LU Q , et al . Negative transfer detection in transductive transfer learning [J ] . International Journal of Machine Learning ＆Cybernetics , 2018 , 9 ( 2 ): 185 - 197 .

ZHENG W L , LYU B L . Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks [J ] . IEEE Transactions on Autonomous Mental Development , 2015 , 7 ( 3 ): 162 - 175 .

DUAN R N , ZHU J Y , LYU B L . Differential entropy feature for EEG-based emotion classification [C ] // Proceedings of the 6th International IEEE/ EMBS Conference on Neural Engineering . Piscataway:IEEE Press , 2013 .

BAI S J , KOLTER J Z , KOLTUN V , et al . An empirical evaluation of generic convolutional and recurrent networks for sequence modeling [J ] . arXiv preprint , 2018 ,arXiv:1803.01271.

WILSON G , DOPPA J R , COOK D . Multi-source deep domain adaptation with weak supervision for time-series sensor data [C ] // Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data . New York:ACM Press , 2020 : 6 - 19 .

FAWAZ H I , FORESTIER G , WEBER J , et al . Deep learning for time series classification:a review [J ] . Data Mining and Knowledge Discovery , 2019 , 33 ( 4 ): 917 - 963 .

XIE Q Z , DAI Z H , DU Y L , et al . Controllbale invariance through adversarial feature learning [J ] . arXiv preprint , 2017 ,arXiv:1705.11122.

GANIN Y , USTINOVA E , AJAKAN H , et al . Domain-adversarial training of neural networks [J ] . Journal of Machine Learning Research , 2017 , 17 ( 1 ): 2096 - 2030 .

WANG Z G , YAN W Z , OATES T . Time series classification from scratch with deep neural networks:a strong baseline [C ] // Proceedings of the 2017 International Joint Conference on Neural Networks . Piscataway:IEEE Press , 2017 .

FAWAZ H I , FORESTIER G , WEBER J , et al . Transfer learning for time series classification [C ] // Proceedings of the 2018 IEEE International Conference on Big Data . Piscataway:IEEE Press , 2018 .

LI H , JIN Y M , ZHENG W L , et al . Cross-subject emotion recognition using deep adaptation networks [C ] // Proceedings of the 25th International Conference on Neural Information Processing . Cham:Springer , 2018 : 403 - 413 .

PAN S J , TSANG I W , KWOK J T , et al . Domain adaptation via transfer component analysis [J ] . IEEE Transactions on Neural Networks , 2011 , 22 ( 2 ): 199 - 210 .

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