- 个人中心
- 退出登录
浏览全部资源
扫码关注微信
Emotional state decoding using EEG-based microstates of functional connectivity
- Chinese Journal of Intelligent Science and Technology Vol. 3, Issue 1, Pages: 49-58(2021)
- 作者机构：
1. 清华大学医学院生物医学工程系，北京 100084
2. 清华大学脑与智能实验室，北京 100084
3. 清华大学社会科学学院心理学系，北京 100084
- 作者简介：
- 基金信息：
DOI：10.11959/j.issn.2096-6652.202105
CLC： R318-
Published Online：2021-03，
Published：15 March 2021
- 稿件说明：
移动端阅览
Xinke SHEN, Yichao LI, Jin LIU, et al. Emotional state decoding using EEG-based microstates of functional connectivity[J]. Chinese journal of intelligent science and technology, 2021, 3(1): 49-58.
Xinke SHEN, Yichao LI, Jin LIU, et al. Emotional state decoding using EEG-based microstates of functional connectivity[J]. Chinese journal of intelligent science and technology, 2021, 3(1): 49-58. DOI： 10.11959/j.issn.2096-6652.202105.
摘要
基于脑电的情绪状态解码大多将个体情绪看作相对稳定的状态，将脑电频域能量、通道间脑电相关性等稳态指标作为解码中使用的特征。基于近年来网络神经科学在脑区间动态功能连接分析中的新发展，设计并实现了功能连接微状态方法，将不同情绪状态下脑区间的动态功能连接模式聚集为具有代表性的微状态，提取微状态的覆盖比例、转移概率等时间动态过程指标作为特征，用于情绪状态解码。基于经典的脑电情绪公开数据集DEAP，动态功能连接微状态新特征在情绪的效价和唤醒两个维度上实现了均方误差分别为3.87±0.28和3.25±0.30的回归预测效力，优于传统频带能量特征的均方误差4.07±0.30（p=0.005）和3.41±0.31（p=0.064）。实验结果展示了基于脑电功能连接微状态的情绪状态解码可行性，并为进一步深入理解情绪的神经机制提供了启发。
Abstract
Emotional state decoding based on electroencephalography (EEG) usually regards individual emotion as a relatively static state and uses spectral power or inter-channel correlations of EEG as features.Based on recent advancement of dynamic functional connectivity analysis in the area of network neuroscience
a method called microstates of functional connectivity was designed and implemented
which clustered the inter-regional functional connectivity patterns of the brain under different emotional states to obtain representative microstates
and the temporal statistics
such as coverage and transition probability were extracted as features for emotional state decoding.Based on a widely used publicly available EEG dataset DEAP
new features in microstates of dynamic functional connectivity analysis achieved regression mean squared errors of 3.87±0.28 and 3.25±0.30 on valence and arousal respectively
which were better than those using traditional spectral power features
4.07±0.30 (p=0.005) and 3.41±0.31 (p=0.064).The results demonstrate the feasibility of emotional state decoding based on microstates of functional connectivity and provide deeper insight into understanding the neural mechanisms of emotion.
关键词
Keywords
references
PICARD R W . Affective computing [M ] . MA : MIT Press , 1997 .
PORIA S , CAMBRIA E , BAJPAI R , et al . A review of affective computing:from unimodal analysis to multimodal fusion [J ] . Information Fusion , 2017 , 37 : 98 - 125 .
HU X , CHEN J J , WANG F , et al . Ten challenges for EEG-based affective computing [J ] . Brain Science Advances , 2019 , 5 ( 1 ): 1 - 20 .
DING Y , HU X , XIA Z Y , et al . Inter-brain EEG feature extraction and analysis for continuous implicit emotion tagging during video watching [J ] . IEEE Transactions on Affective Computing , 2018 , 12 ( 1 ): 92 - 102 .
HU X , YU J W , SONG M D , et al . EEG correlates of ten positive emotions [J ] . Frontiers in Human Neuroscience , 2017 , 11 : 26 .
SCHMIDT L A , TRAINOR L J . Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions [J ] . Cognition and Emotion , 2001 , 15 ( 4 ): 487 - 500 .
ZHENG W L , ZHU J Y , LYU B L . Identifying stable patterns over time for emotion recognition from EEG [J ] . IEEE Transactions on Affective Computing , 2017 , 10 ( 3 ): 417 - 429 .
PETRANTONAKIS P C , HADJILEONTIADIS L J . Emotion recognition from EEG using higher order crossings [J ] . IEEE Transactions on Information Technology in Biomedicine , 2009 , 14 ( 2 ): 186 - 97 .
LI P Y , LIU H A , SI Y J , et al . EEG based emotion recognition by combining functional connectivity network and local activations [J ] . IEEE Transactions on Biomedical Engineering , 2019 , 66 ( 10 ): 2869 - 2881 .
LEE Y Y , HSIEH S . Classifying different emotional states by means of EEG-based functional connectivity patterns [J ] . PLOS One , 2014 , 9 ( 4 ): e95415 .
WU X , ZHENG W L , LYU B L . Investigating EEG-based functional connectivity patterns for multimodal emotion recognition [J ] . arXiv preprint , 2020 ,arXiv:2004.01973.
SONG T F , ZHENG W M , SONG P , et al . EEG emotion recognition using dynamical graph convolutional neural networks [J ] . IEEE Transactions on Affective Computing , 2018 , 11 ( 3 ): 532 - 541 .
BASSETT D S , SPORNS O . Network neuroscience [J ] . Nature Neuroscience , 2017 , 20 ( 3 ): 353 - 364 .
O'NEILL G C , TEWARIE P , VIDAURRE D , et al . Dynamics of large-scale electrophysiological networks:a technical review [J ] . Neuroimage , 2018 , 180 : 559 - 576 .
LU Q , BI K , LIU C , et al . Predicting depression based on dynamic regional connectivity:a windowed Granger causality analysis of MEG recordings [J ] . Brain Research , 2013 , 1535 : 52 - 60 .
CETIN M S , HOUCK J M , RASHID B , et al . Multimodal classification of schizophrenia patients with MEG and fMRI data using static and dynamic connectivity measures [J ] . Frontiers in Neuroscience , 2016 , 10 : 466 .
LEE H , NOH G J , JOO P , et al . Diversity of functional connectivity patterns is reduced in propofol-induced unconsciousness [J ] . Human Brain Mapping , 2017 , 38 ( 10 ): 4980 - 4995 .
MCMENAMIN B W , LANGESLAG S J , SIRBU M , et al . Network organization unfolds over time during periods of anxious anticipation [J ] . Journal of Neuroscience , 2014 , 34 ( 34 ): 11261 - 11273 .
HERMANS E J , HENCKENS M J , JOËLS M , et al . Dynamic adaptation of large-scale brain networks in response to acute stressors [J ] . Trends in Neurosciences , 2014 , 37 ( 6 ): 304 - 314 .
PESSOA L , MCMENAMIN B J . Dynamic networks in the emotional brain [J ] . The Neuroscientist , 2017 , 23 ( 4 ): 383 - 396 .
MICHEL C M , KOENIG T J . EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks:a review [J ] . Neuroimage , 2018 , 180 : 577 - 593 .
LEHMANN D , FABER P L , GALDERISI S , et al . EEG microstate duration and syntax in acute,medication-naive,first-episode schizophrenia:a multi-center study [J ] . Psychiatry Research:Neuroimaging , 2005 , 138 ( 2 ): 141 - 156 .
AL ZOUBI O , MAYELI A , TSUCHIYAGAITO A , et al . EEG microstates temporal dynamics differentiate individuals with mood and anxiety disorders from healthy subjects [J ] . Frontiers in Human Neuroscience , 2019 , 13 : 56 .
NISHIDA K , MORISHIMA Y , YOSHIMURA M , et al . EEG microstates associated with salience and frontoparietal networks in frontotemporal dementia,schizophrenia and Alzheimer’s disease [J ] . Clinical Neurophysiology , 2013 , 124 ( 6 ): 1106 - 1114 .
MILZ P , FABER P L , LEHMANN D , et al . The functional significance of EEG microstates—associations with modalities of thinking [J ] . Neuroimage , 2016 , 125 : 643 - 656 .
DUC N T , LEE B J . Microstate functional connectivity in EEG cognitive tasks revealed by a multivariate Gaussian hidden Markov model with phase locking value [J ] . Journal of Neural Engineering , 2019 , 16 ( 2 ): 026033 .
KOELSTRA S , MUHL C , SOLEYMANI M , et al . DEAP:a database for emotion analysis; using physiological signals [J ] . IEEE Transactions on Affective Computing , 2011 , 3 ( 1 ): 18 - 31 .
RUSSELL J A . A circumplex model of affect [J ] . Journal of Personality and Social Psychology , 1980 , 39 ( 6 ): 1161 - 1178 .
YAN J Z , CHEN S B , DENG S N . A EEG-based emotion recognition model with rhythm and time characteristics [J ] . Brain Informatics , 2019 , 6 ( 1 ): 7 .
OOSTENVELD R , FRIES P , MARIS E , et al . FieldTrip:open source software for advanced analysis of MEG,EEG,and invasive electrophysiological data [J ] . Computational Intelligence and Neuroscience , 2011 : 15869 .
ARTHUR D , VASSILVITSKII S . K-means++:the advantages of careful seeding [C ] // Proceeding of the 8th Annual ACM-SIAM Symposium on Discrete Algorithms . New York:ACM Press , 2007 .
WU W , ZHANG Y , JIANG J , et al . An electroencephalographic signature predicts antidepressant response in major depression [J ] . Nature Biotechnology , 2020 , 38 ( 4 ): 439 - 447 .
KATSIGIANNIS S , RAMZAN N . DREAMER:a database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices [J ] . IEEE Journal of Biomedical and Health Informatics , 2017 , 22 ( 1 ): 98 - 107 .
SHEN X K , HU X , LIU S Z , et al . Exploring EEG microstates for affective computing:decoding valence and arousal experiences during video watching [C ] // Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society . Piscataway:IEEE Press , 2020 : 841 - 846 .
REINEN J M , CHÉN O Y , HUTCHISON R M , et al . The human cortex possesses a reconfigurable dynamic network architecture that is disrupted in psychosis [J ] . Nature Communications , 2018 , 9 ( 1 ): 1 - 15 .
STAM C J , NOLTE G , DAFFERTSHOFER A J . Phase lag index:assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources [J ] . Human Brain Mapping , 2007 , 28 ( 11 ): 1178 - 1193 .
0
Views
1190
下载量
0
CSCD
- L-PDFDownload
- XMLDownload
- Meta-XMLDownload
- BibTeXDownload
- EndnoteDownload
- RefMan Download
- NoteExpressDownload
- NoteFirstDownload
Publicity Resources
Related Articles
Related Author
Related Institution
- Postal code：100079
- Tel：（010）53879206 Email：cjist@bjxintong.com.cn
- Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19
京公网安备11010802024621 - It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
- Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
- Total Visits：35685 Visits Today：160
