一种面向机器情感智能的人格引导型情感脑机接口

李少杰; 李伟; 邢泽健; 袁文杰; 韦香玉; 张晓炜; 胡斌

doi:10.1631/FITEE.2100489

Your Location：

Home >

Browse articles >

一种面向机器情感智能的人格引导型情感脑机接口

人机协同与认知计算专题 | Updated：2022-09-08

- 一种面向机器情感智能的人格引导型情感脑机接口
- A personality-guided affective brain–computer interface for implementation of emotional intelligence in machines
- 信息与电子工程前沿（英文） 2022年23卷第8期页码：1158-1173
- Affiliations：
  
  School of Information Science and Engineering, Lanzhou University, Lanzhou 730099, China
- Author bio：
  
  ‡Corresponding authors
- Funds：
  
  National Key R&D Program of China(2019YFA0706200);National Natural Science Foundation of China(62072219;61632014);National Basic Research Program (973) of China(2014CB744600)
- DOI：10.1631/FITEE.2100489
  中图分类号： TP39
- 收稿：2021-10-14，
  
  录用：2022-07-28，
  
  纸质出版：2022-08-23
- Accepted：
Scan QR Code
李少杰, 李伟, 邢泽健, 等. 一种面向机器情感智能的人格引导型情感脑机接口[J]. 信息与电子工程前沿（英文）, 2022,23(8):1158-1173.

Shaojie LI, Wei LI, Zejian XING, et al. A personality-guided affective brain–computer interface for implementation of emotional intelligence in machines[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(8): 1158-1173.
李少杰, 李伟, 邢泽健, 等. 一种面向机器情感智能的人格引导型情感脑机接口[J]. 信息与电子工程前沿（英文）, 2022,23(8):1158-1173. DOI： 10.1631/FITEE.2100489.

Shaojie LI, Wei LI, Zejian XING, et al. A personality-guided affective brain–computer interface for implementation of emotional intelligence in machines[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(8): 1158-1173. DOI： 10.1631/FITEE.2100489.

摘要

情感脑机接口（brain–computer interfaces

BCIs）已成为在人机协作中实现情感智能的一个重要途径。然而，由于脑电图（electroencephalogram

EEG）信号的复杂性和情绪反应的个体差异性，设计一个可靠和有效的模型仍然是一个巨大挑战。考虑到不同人格特征的个体在情绪感知和反应过程中的差异，整合人格信息和脑电信号对情绪识别是有帮助的。鉴于此，提出一种人格引导的注意力神经网络，其可以利用人格信息学习更为有效的EEG表征以用于情感识别。具体来说，我们首先利用卷积神经网络提取脑电信号的时域和空域表征，进而设计一种特殊的卷积核同时学习大脑头皮不同区域间和区域内的EEG导联相关关系。其次，考虑到不同大脑头皮区域在情绪识别中可能发挥不同的作用，提出一种人格引导的区域注意力机制，以进一步探索区域内和区域间EEG导联的贡献。最后，设计一种基于注意力的长短期记忆网络（long short-term memory

LSTM）建模EEG信号的时域动态特征。在AMIGOS数据集（一个用于个人和群体的情感、人格特征和情绪多模态研究的数据集）的实验结果表明，本研究所提方法可以显著提升被试独立策略下情感识别的性能，并优于现有情感识别方法。

Abstract

Affective brain–computer interfaces have become an increasingly important topic to achieve emotional intelligence in human–machine collaboration. However

due to the complexity of electroencephalogram (EEG) signals and the individual differences in emotional response

it is still a great challenge to design a reliable and effective model. Considering the influence of personality traits on emotional response

it would be helpful to integrate personality information and EEG signals for emotion recognition. This study proposes a personality-guided attention neural network that can use personality information to learn effective EEG representations for emotion recognition. Specifically

we first use a convolutional neural network to extract rich temporal and regional representations of EEG signals

and a special convolution kernel is designed to learn inter- and intra-regional correlations simultaneously. Second

inspired by the fact that electrodes within distinct brain scalp regions play different roles in emotion recognition

a personality-guided regional-attention mechanism is proposed to further explore the contributions of electrodes within a region and between regions. Finally

attention-based long short-term memory is designed to explore the temporal dynamics of EEG signals. Experiments on the AMIGOS dataset

which is a dataset for multimodal research for affect

personality traits

and mood on individuals and groups

show that the proposed method can significantly improve the performance of subject-independent emotion recognition and outperform state-of-the-art methods.

关键词

Keywords

references

Alarcão SM , Fonseca MJ , 2019 . Emotions recognition using EEG signals: a survey . IEEE Trans Affect Comput , 10 ( 3 ): 374 - 393 . doi: 10.1109/TAFFC.2017.2714671 http://doi.org/10.1109/TAFFC.2017.2714671

Alhagry S , Fahmy AA , El-Khoribi RA , 2017 . Emotion recognition based on EEG using LSTM recurrent neural network . Int J Adv Comput Sci Appl , 8 ( 10 ): 355 - 358 . doi: 10.14569/IJACSA.2017.081046 http://doi.org/10.14569/IJACSA.2017.081046

Alhussein M , 2016 . Automatic facial emotion recognition using Weber local descriptor for e-Healthcare system . Clust Comput , 19 ( 1 ): 99 - 108 . doi: 10.1007/s10586-016-0535-3 http://doi.org/10.1007/s10586-016-0535-3

Ayata D , Yaslan Y , Kamasak ME , 2018 . Emotion based music recommendation system using wearable physiological sensors . IEEE Trans Consum Electron , 64 ( 2 ): 196 - 203 . doi: 10.1109/TCE.2018.2844736 http://doi.org/10.1109/TCE.2018.2844736

Barkana BD , Ozkan Y , Badara JA , 2022 . Analysis of working memory from EEG signals under different emotional states . Biomed Signal Process Contr , 71 : 103249 . doi: 10.1016/j.bspc.2021.103249 http://doi.org/10.1016/j.bspc.2021.103249

Bhardwaj A , Gupta A , Jain P , et al. , 2015 . Classification of human emotions from EEG signals using SVM and LDA classifiers . 2 nd Int Conf on Signal Processing and Integrated Networks , p. 180 - 185 . doi: 10.1109/SPIN.2015.7095376 http://doi.org/10.1109/SPIN.2015.7095376

Chaplin TM , 2015 . Gender and emotion expression: a developmental contextual perspective . Emot Rev , 7 ( 1 ): 14 - 21 . doi: 10.1177/1754073914544408 http://doi.org/10.1177/1754073914544408

Chen H , Song Y , Li XL , 2019 . A deep learning framework for identifying children with ADHD using an EEG-based brain network . Neurocomputing , 356 : 83 - 96 . doi: 10.1016/j.neucom.2019.04.058 http://doi.org/10.1016/j.neucom.2019.04.058

Chevalier P , Martin JC , Isableu B , et al. , 2015 . Impact of personality on the recognition of emotion expressed via human, virtual, and robotic embodiments . 24 th IEEE Int Symp on Robot and Human Interactive Communication , p. 229 - 234 . doi: 10.1109/ROMAN.2015.7333686 http://doi.org/10.1109/ROMAN.2015.7333686

Cui H , Liu AP , Zhang X , et al. , 2020 . EEG-based emotion recognition using an end-to-end regional-asymmetric convolutional neural network . Knowl Based Syst , 205 : 106243 . doi: 10.1016/j.knosys.2020.106243 http://doi.org/10.1016/j.knosys.2020.106243

Ding Y , Robinson N , Zeng QH , et al. , 2020 . TSception: a deep learning framework for emotion detection using EEG . Int Joint Conf on Neural Networks , p. 1 - 7 . doi: 10.1109/IJCNN48605.2020.9206750 http://doi.org/10.1109/IJCNN48605.2020.9206750

Ding Y , Hu X , Xia ZY , et al. , 2021 . Inter-brain EEG feature extraction and analysis for continuous implicit emotion tagging during video watching . IEEE Trans Affect Comput , 12 ( 1 ): 92 - 102 . doi: 10.1109/TAFFC.2018.2849758 http://doi.org/10.1109/TAFFC.2018.2849758

Ding Y , Robinson N , Zhang S , et al. , 2022 . TSception: capturing temporal dynamics and spatial asymmetry from EEG for emotion recognition . IEEE Trans Affect Comput , early access . doi: 10.1109/TAFFC.2022.3169001 http://doi.org/10.1109/TAFFC.2022.3169001

Duan RN , Zhu JY , Lu BL , 2013 . Differential entropy feature for EEG-based emotion classification . 6 th Int IEEE/EMBS Conf on Neural Engineering , p. 81 - 84 . doi: 10.1109/NER.2013.6695876 http://doi.org/10.1109/NER.2013.6695876

Etkin A , Egner T , Kalisch R , 2011 . Emotional processing in anterior cingulate and medial prefrontal cortex . Trends Cogn Sci , 15 ( 2 ): 85 - 93 . doi: 10.1016/j.tics.2010.11.004 http://doi.org/10.1016/j.tics.2010.11.004

Farahat A , Reichert C , Sweeney-Reed CM , et al. , 2019 . Convolutional neural networks for decoding of covert attention focus and saliency maps for EEG feature visualization . J Neur Eng , 16 ( 6 ): 066010 . doi: 10.1088/1741-2552/ab3bb4 http://doi.org/10.1088/1741-2552/ab3bb4

Fiterau M , Bhooshan S , Fries J , et al. , 2017 . ShortFuse: biomedical time series representations in the presence of structured information . Proc 2 nd Machine Learning for Healthcare Conf , p. 59 - 74 .

Fossum TA , Barrett LF , 2000 . Distinguishing evaluation from description in the personality-emotion relationship . Pers Soc Psychol Bull , 26 ( 6 ): 669 - 678 . doi: 10.1177/0146167200268003 http://doi.org/10.1177/0146167200268003

Furnes D , Berg H , Mitchell RM , et al. , 2019 . Exploring the effects of personality traits on the perception of emotions from prosody . Front Psychol , 10 : 184 . doi: 10.3389/fpsyg.2019.00184 http://doi.org/10.3389/fpsyg.2019.00184

He KM , Zhang XY , Ren SQ , et al. , 2016 . Deep residual learning for image recognition . IEEE Conf on Computer Vision and Pattern Recognition , p. 770 - 778 . doi: 10.1109/CVPR.2016.90 http://doi.org/10.1109/CVPR.2016.90

Kehoe EG , Toomey JM , Balsters JH , et al. , 2012 . Personality modulates the effects of emotional arousal and valence on brain activation . Soc Cogn Affect Neurosci , 7 ( 7 ): 858 - 870 . doi: 10.1093/scan/nsr059 http://doi.org/10.1093/scan/nsr059

Klados MA , Konstantinidi P , Dacosta-Aguayo R , et al. , 2020 . Automatic recognition of personality profiles using EEG functional connectivity during emotional processing . Brain Sci , 10 ( 5 ): 278 . doi: 10.3390/brainsci10050278 http://doi.org/10.3390/brainsci10050278

Koelstra S , Muhl C , Soleymani M , et al. , 2012 . DEAP: a database for emotion analysis using physiological signals . IEEE Trans Affect Comput , 3 ( 1 ): 18 - 31 . doi: 10.1109/T-AFFC.2011.15 http://doi.org/10.1109/T-AFFC.2011.15

Kragel PA , LaBar KS , 2016 . Decoding the nature of emotion in the brain . Trends Cogn Sci , 20 ( 6 ): 444 - 455 . doi: 10.1016/j.tics.2016.03.011 http://doi.org/10.1016/j.tics.2016.03.011

Lan ZR , Sourina O , Wang LP , et al. , 2016 . Real-time EEG-based emotion monitoring using stable features . Vis Comput , 32 ( 3 ): 347 - 358 . doi: 10.1007/s00371-015-1183-y http://doi.org/10.1007/s00371-015-1183-y

Larsen RJ , Ketelaar T , 1991 . Personality and susceptibility to positive and negative emotional states . J Pers Soc Psychol , 61 ( 1 ): 132 - 140 . doi: 10.1037/0022-3514.61.1.132 http://doi.org/10.1037/0022-3514.61.1.132

Lawhern VJ , Solon AJ , Waytowich NR , et al. , 2018 . EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces . J Neur Eng , 15 ( 5 ): 056013 . doi: 10.1088/1741-2552/aace8c http://doi.org/10.1088/1741-2552/aace8c

Li WY , Hu X , Long XF , et al. , 2020 . EEG responses to emotional videos can quantitatively predict Big-Five personality traits . Neurocomputing , 415 : 368 - 381 . doi: 10.1016/j.neucom.2020.07.123 http://doi.org/10.1016/j.neucom.2020.07.123

Li X , Song DW , Zhang P , et al. , 2016 . Emotion recognition from multi-channel EEG data through convolutional recurrent neural network . IEEE Int Conf on Bioinformatics and Biomedicine , p. 352 - 359 . doi: 10.1109/BIBM.2016.7822545 http://doi.org/10.1109/BIBM.2016.7822545

Li Y , Zheng WM , Zong Y , et al. , 2021 . A bi-hemisphere domain adversarial neural network model for EEG emotion recognition . IEEE Trans Affect Comput , 12 ( 2 ): 494 - 504 . doi: 10.1109/TAFFC.2018.2885474 http://doi.org/10.1109/TAFFC.2018.2885474

Li Y , Zheng WM , Wang L , et al. , 2022 . From regional to global brain: a novel hierarchical spatial-temporal neural network model for EEG emotion recognition . IEEE Trans Affect Comput , 13 ( 2 ): 568 - 578 . doi: 10.1109/TAFFC.2019.2922912 http://doi.org/10.1109/TAFFC.2019.2922912

Lindquist KA , Barrett LF , 2012 . A functional architecture of the human brain: emerging insights from the science of emotion . Trends Cogn Sci , 16 ( 11 ): 533 - 540 . doi: 10.1016/j.tics.2012.09.005 http://doi.org/10.1016/j.tics.2012.09.005

Lotfi E , Akbarzadeh-T MR , 2014 . Practical emotional neural networks . Neur Netw , 59 : 61 - 72 . doi: 10.1016/j.neunet.2014.06.012 http://doi.org/10.1016/j.neunet.2014.06.012

Ma WF , Gong YF , Zhou GX , et al. , 2021 . A channel-mixing convolutional neural network for motor imagery EEG decoding and feature visualization . Biomed Signal Process Contr , 70 : 103021 . doi: 10.1016/j.bspc.2021.103021 http://doi.org/10.1016/j.bspc.2021.103021

Maaoui C , Pruski A , 2010 . Emotion recognition through physiological signals for human-machine communication . In: Kordic V (Ed.), Cutting Edge Robotics 2010 . IntechOpen , Rijeka, Croatia . doi: 10.5772/10312 http://doi.org/10.5772/10312

Martínez-Tejada LA , Maruyama Y , Yoshimura N , et al. , 2020 . Analysis of personality and EEG features in emotion recognition using machine learning techniques to classify arousal and valence labels . Mach Learn Knowl Extr , 2 ( 2 ): 99 - 124 . doi: 10.3390/make2020007 http://doi.org/10.3390/make2020007

Miranda-Correa JA , Abadi MK , Sebe N , et al. , 2021 . AMIGOS: a dataset for affect, personality and mood research on individuals and groups . IEEE Trans Affect Comput , 12 ( 2 ): 479 - 493 . doi: 10.1109/TAFFC.2018.2884461 http://doi.org/10.1109/TAFFC.2018.2884461

Mohammadi Z , Frounchi J , Amiri M , 2017 . Wavelet-based emotion recognition system using EEG signal . Neur Comput Appl , 28 ( 8 ): 1985 - 1990 . doi: 10.1007/s00521-015-2149-8 http://doi.org/10.1007/s00521-015-2149-8

Mühl C , Allison B , Nijholt A , et al. , 2014 . A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges . Brain Comput Interf , 1 ( 2 ): 66 - 84 . doi: 10.1080/2326263X.2014.912881 http://doi.org/10.1080/2326263X.2014.912881

Niemic CP , 2002 . Studies of emotion: a theoretical and empirical review of psychophysiological studies of emotion . J Undergrad Res , 1 : 15 - 18 .

Orgeta V , 2009 . Specificity of age differences in emotion regulation . Aging Ment Health , 13 ( 6 ): 818 - 826 . doi: 10.1080/13607860902989661 http://doi.org/10.1080/13607860902989661

Özerdem MS , Polat H , 2017 . Emotion recognition based on EEG features in movie clips with channel selection . Brain Inform , 4 ( 4 ): 241 - 252 . doi: 10.1007/s40708-017-0069-3 http://doi.org/10.1007/s40708-017-0069-3

Perugini M , Di Blas L , 2002 . Analyzing personality related adjectives from an eticemic perspective: the Big Five marker scales (BFMS) and the Italian AB5C taxonomy . In: de Raad B , Perugini M (Eds.), Big Five Assessment . Hogrefe & Huber Publishers , Seattle, USA , p. 281 - 304 .

Picard RW , Vyzas E , Healey J , 2001 . Toward machine emotional intelligence: analysis of affective physiological state . IEEE Trans Patt Anal Mach Intell , 23 ( 10 ): 1175 - 1191 . doi: 10.1109/34.954607 http://doi.org/10.1109/34.954607

Rao HC , Wang SQ , Hu XP , et al. , 2021 . A self-supervised gait encoding approach with locality-awareness for 3D skeleton based person re-identification . IEEE Trans Patt Anal Mach Intell , early access . doi: 10.1109/TPAMI.2021.3092833 http://doi.org/10.1109/TPAMI.2021.3092833

Revelle WR , Scherer KR , 2009 . Personality and emotion . In: Sander D , Scherer KR (Eds.), Oxford Companion to Emotion and the Affective Sciences . Oxford University Press , Oxford, UK , p. 304 - 305 .

Rukavina S , Gruss S , Hoffmann H , et al. , 2016 . Affective computing and the impact of gender and age . PLoS ONE , 11 ( 3 ): e0150584 . doi: 10.1371/journal.pone.0150584 http://doi.org/10.1371/journal.pone.0150584

Salovey P , Mayer JD , 1990 . Emotional intelligence . Imaginat Cognit Pers , 9 ( 3 ): 185 - 211 . doi: 10.2190/DUGG-P24E-52WK-6CDG http://doi.org/10.2190/DUGG-P24E-52WK-6CDG

Schirrmeister RT , Springenberg JT , Fiederer LDJ , et al. , 2017 . Deep learning with convolutional neural networks for EEG decoding and visualization . Hum Brain Mapp , 38 ( 11 ): 5391 - 5420 . doi: 10.1002/hbm.23730 http://doi.org/10.1002/hbm.23730

Schmidtke JI , Heller W , 2004 . Personality, affect and EEG: predicting patterns of regional brain activity related to extraversion and neuroticism . Pers Individ Differ , 36 ( 3 ): 717 - 732 . doi: 10.1016/S0191-8869(03)00129-6 http://doi.org/10.1016/S0191-8869(03)00129-6

Shen J , Zhang XW , Wang G , et al. , 2022 . An improved empirical mode decomposition of electroencephalogram signals for depression detection . IEEE Trans Affect Comput , 13 ( 1 ): 262 - 271 . doi: 10.1109/TAFFC.2019.2934412 http://doi.org/10.1109/TAFFC.2019.2934412

Shen T , Zhou TY , Long GD , et al. , 2018 . DiSAN: directional self-attention network for RNN/CNN-free language understanding . Proc 32 nd AAAI Conf on Artificial Intelligence , p. 5446 - 5455 . doi: 10.1609/aaai.v32i1.11941 http://doi.org/10.1609/aaai.v32i1.11941

Shu YY , Wang SF , 2017 . Emotion recognition through integrating EEG and peripheral signals . IEEE Int Conf on Acoustics, Speech and Signal Processing , p. 2871 - 2875 . doi: 10.1109/ICASSP.2017.7952681 http://doi.org/10.1109/ICASSP.2017.7952681

Steinert S , Friedrich O , 2020 . Wired emotions: ethical issues of affective brain-computer interfaces . Sci Eng Ethics , 26 ( 1 ): 351 - 367 . doi: 10.1007/s11948-019-00087-2 http://doi.org/10.1007/s11948-019-00087-2

Topic A , Russo M , Stella M , et al. , 2022 . Emotion recognition using a reduced set of EEG channels based on holographic feature maps . Sensors , 22 ( 9 ): 3248 . doi: 10.3390/s22093248 http://doi.org/10.3390/s22093248

van Leeuwen KG , Sun H , Tabaeizadeh M , et al. , 2019 . Detecting abnormal electroencephalograms using deep convolutional networks . Clin Neurophysiol , 130 ( 1 ): 77 - 84 . doi: 10.1016/j.clinph.2018.10.012 http://doi.org/10.1016/j.clinph.2018.10.012

Vaswani A , Shazeer N , Parmar N , et al. , 2017 . Attention is all you need . Proc 32 st Int Conf on Neural Information Processing Systems , p. 6000 - 6010 .

Vuoskoski JK , Eerola T , 2011 . The role of mood and personality in the perception of emotions represented by music . Cortex , 47 ( 9 ): 1099 - 1106 . doi: 10.1016/j.cortex.2011.04.011 http://doi.org/10.1016/j.cortex.2011.04.011

Waytowich N , Lawhern VJ , Garcia JO , et al. , 2018 . Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials . J Neur Eng , 15 ( 6 ): 066031 . doi: 10.1088/1741-2552/aae5d8 http://doi.org/10.1088/1741-2552/aae5d8

Wen ZY , Xu RF , Du JC , 2017 . A novel convolutional neural networks for emotion recognition based on EEG signal . Int Conf on Security, Pattern Analysis, and Cybernetics , p. 672 - 677 . doi: 10.1109/SPAC.2017.8304360 http://doi.org/10.1109/SPAC.2017.8304360

Wolpaw JR , Birbaumer N , McFarland DJ , et al. , 2002 . Brain–computer interfaces for communication and control . Clin Neurophysiol , 113 ( 6 ): 767 - 791 . doi: 10.1016/S1388-2457(02)00057-3 http://doi.org/10.1016/S1388-2457(02)00057-3

Xu SH , Rao HC , Peng H , et al. , 2021 . Attention-based multilevel co-occurrence graph convolutional LSTM for 3-D action recognition . IEEE Internet Things J , 8 ( 21 ): 15990 - 16001 . doi: 10.1109/JIOT.2020.3042986 http://doi.org/10.1109/JIOT.2020.3042986

Yang YL , Wu QF , Qiu M , et al. , 2018 . Emotion recognition from multi-channel EEG through parallel convolutional recurrent neural network . Int Joint Conf on Neural Networks , p. 1 - 7 . doi: 10.1109/IJCNN.2018.8489331 http://doi.org/10.1109/IJCNN.2018.8489331

Zhang DL , Yao LN , Zhang X , et al. , 2018 . Cascade and parallel convolutional recurrent neural networks on EEG-based intention recognition for brain computer interface . Proc 32 nd AAAI Conf on Artificial Intelligence , p. 1703 - 1710 . doi: 10.1609/aaai.v32i1.11496 http://doi.org/10.1609/aaai.v32i1.11496

Zhang DL , Yao LN , Chen KX , et al. , 2019 . A convolutional recurrent attention model for subject-independent EEG signal analysis . IEEE Signal Process Lett , 26 ( 5 ): 715 - 719 . doi: 10.1109/LSP.2019.2906824 http://doi.org/10.1109/LSP.2019.2906824

Zhang GH , Yu MJ , Liu YJ , et al. , 2021 . SparseDGCNN: recognizing emotion from multichannel EEG signals . IEEE Trans Affect Comput , early access . doi: 10.1109/TAFFC.2021.3051332 http://doi.org/10.1109/TAFFC.2021.3051332

Zhang T , Cui Z , Xu CY , et al. , 2020 . Variational pathway reasoning for EEG emotion recognition . Proc 34 th AAAI Conf on Artificial Intelligence , 2709 - 2716 . doi: 10.1609/aaai.v34i03.5657 http://doi.org/10.1609/aaai.v34i03.5657

Zhang XW , Li JL , Hou KC , et al. , 2020 . EEG-based depression detection using convolutional neural network with demographic attention mechanism . 42 nd Annual Int Conf of the IEEE Engineering in Medicine & Biology Society , p. 128 - 133 . doi: 10.1109/EMBC44109.2020.9175956 http://doi.org/10.1109/EMBC44109.2020.9175956

Zhang XW , Liu JY , Shen J , et al. , 2021 . Emotion recognition from multimodal physiological signals using a regularized deep fusion of kernel machine . IEEE Trans Cybern , 51 ( 9 ): 4386 - 4399 . doi: 10.1109/TCYB.2020.2987575 http://doi.org/10.1109/TCYB.2020.2987575

Zhang XW , Pan J , Shen J , et al. , 2022 . Fusing of electroencephalogram and eye movement with group sparse canonical correlation analysis for anxiety detection . IEEE Trans Affect Comput , 13 ( 2 ): 958 - 971 . doi: 10.1109/TAFFC.2020.2981440 http://doi.org/10.1109/TAFFC.2020.2981440

Zhang YH , Prasad S , Kilicarslan A , et al. , 2017 . Multiple kernel based region importance learning for neural classification of gait states from EEG signals . Front Neurosci , 11 : 170 . doi: 10.3389/fnins.2017.00170 http://doi.org/10.3389/fnins.2017.00170

Zhao GZ , Zhang YL , Ge Y , et al. , 2018a . Asymmetric hemisphere activation in tenderness: evidence from EEG signals . Sci Rep , 8 ( 1 ): 8029 . doi: 10.1038/s41598-018-26133-w http://doi.org/10.1038/s41598-018-26133-w

Zhao GZ , Ge Y , Shen BY , et al. , 2018b . Emotion analysis for personality inference from EEG signals . IEEE Trans Affect Comput , 9 ( 3 ): 362 - 371 . doi: 10.1109/TAFFC.2017.2786207 http://doi.org/10.1109/TAFFC.2017.2786207

Zhao GZ , Zhang YL , Ge Y , 2018c . Frontal EEG asymmetry and middle line power difference in discrete emotions . Front Behav Neurosci , 12 : 225 . doi: 10.3389/fnbeh.2018.00225 http://doi.org/10.3389/fnbeh.2018.00225

Zhao SC , Ding GG , Han JG , et al. , 2018 . Personality-aware personalized emotion recognition from physiological signals . Proc 27 th Int Joint Conf on Artificial Intelligence , p. 1660 - 1667 . doi: 10.24963/ijcai.2018/230 http://doi.org/10.24963/ijcai.2018/230

Zheng WL , Lu BL , 2015 . Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks . IEEE Trans Auton Ment Dev , 7 ( 3 ): 162 - 175 . doi: 10.1109/TAMD.2015.2431497 http://doi.org/10.1109/TAMD.2015.2431497

浏览量

120

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

Attention-based efficient robot grasp detection network

Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation

Using psychophysiological measures to recognize personal music emotional experience

Using Kinect for real-time emotion recognition via facial expressions