基于多源域自适应的跨被试情感脑电识别

doi:10.11959/j.issn.2096-6652.202106

智能科学与技术学报 ›› 2021, Vol. 3 ›› Issue (1): 59-64.doi: 10.11959/j.issn.2096-6652.202106

基于多源域自适应的跨被试情感脑电识别

高寒冰¹, 张驰¹, 靳明艳¹, 肖杨¹, 丛丰裕¹^,²

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

修回日期:2021-02-24 出版日期:2021-03-15 发布日期:2021-03-01
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。
基金资助:
国家自然科学基金资助项目(61703069);国家重点基础研究发展计划资金资助项目(JCKY2019110B009)

Cross-subject emotional EEG recognition based on multi-source domain adaptation

Hanbing GAO¹, Chi ZHANG¹, Mingyan JIN¹, Yang XIAO¹, Fengyu CONG¹^,²

¹ School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
² School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China

Revised:2021-02-24 Online:2021-03-15 Published:2021-03-01
Supported by:
The National Natural Science Foundation of China(61703069);The National Key Basic Research Program of China(JCKY2019110B009)

摘要/Abstract

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。

关键词: 多源域, 跨被试, 域自适应, 神经网络, 情感脑机接口, 脑电信号

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.

Key words: multi-source domain, cross-subject, domain adaptation, neural network, affective brain-computer interface, EEG signal

中图分类号:

R318

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.

图/表 4

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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

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