Cross subject personality assessment based on electroencephalogram functional connectivity and domain adaptation_Journal of Biomedical Engineering

Authors：

XU Ziming ^1,2 , ZHOU Yueying ^1,2 , WEN Xuyun ^1,2 , NIU Yifan ^3,4 , LI Ziyu ^3,4 , XU Xijia ⁵ ,  ZHANG Daoqiang ^1,2 ,  WU Xia ^3,4

1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, P. R. China;
2. MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, P. R. China;
3. College of Artificial Intelligence, Beijing Normal University, Beijing 100875, P. R. China;
4. MOE Engineering Research Center for Intelligent Technology and Educational Application, Beijing Normal University, Beijing 100875, P. R. China;
5. Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 211106, P. R. China;

Corresponding author：

ZHANG Daoqiang, Email: dqzhang@nuaa.edu.cn; WU Xia, Email: wuxia@bnu.edu.cn

Keywords：

Electroencephalogram signal; Functional connectivity; Cross subject; Personality assessment; Domain adaptation

DOI：

10.7507/1001-5515.202105033

Video：

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Abstract Full text Figures/Tables Video References Cited by

The research shows that personality assessment can be achieved by regression model based on electroencephalogram (EEG). Most of existing researches use event-related potential or power spectral density for personality assessment, which can only represent the brain information of a single region. But some research shows that human cognition is more dependent on the interaction of brain regions. In addition, due to the distribution difference of EEG features among subjects, the trained regression model can not get accurate results of cross subject personality assessment. In order to solve the problem, this research proposes a personality assessment method based on EEG functional connectivity and domain adaption. This research collected EEG data from 45 normal people under different emotional pictures (positive, negative and neutral). Firstly, the coherence of 59 channels in 5 frequency bands was taken as the original feature set. Then the feature-based domain adaptation was used to map the feature to a new feature space. It can reduce the distribution difference between training and test set in the new feature space, so as to reduce the distribution difference between subjects. Finally, the support vector regression model was trained and tested based on the transformed feature set by leave-one-out cross-validation. What’s more, this paper compared the methods used in previous researches. The results showed that the method proposed in this paper improved the performance of regression model and obtained better personality assessment results. This research provides a new method for personality assessment.

Citation： XU Ziming, ZHOU Yueying, WEN Xuyun, NIU Yifan, LI Ziyu, XU Xijia, ZHANG Daoqiang, WU Xia. Cross subject personality assessment based on electroencephalogram functional connectivity and domain adaptation. Journal of Biomedical Engineering, 2022, 39(2): 257-266. doi: 10.7507/1001-5515.202105033 Copy

1.	陈少华. 人格心理学. 广州: 暨南大学出版社, 2010.
2.	Eysenck H J, Eysenck S B G. Manual of the Eysenck personality questionnaire. Londa: Hodder and Stoughton Educational, 1975.
3.	Yang J, McCrae R R, Costa P T Jr, et al. Cross-cultural personality assessment in psychiatric populations: the NEO-PI-R in the People's Republic of China. Psychol Assess, 1999, 11(3): 359-368.
4.	Ashton M C, Lee K, Perugini M, et al. A six-factor structure of personality-descriptive adjectives: solutions from psycholexical studies in seven languages. J Pers Soc Psychol, 2004, 86(2): 356-366.
5.	Cloninger C R, Svrakic D M, Przybeck T R. A psychobiological model of temperament and character. Arch Gen Psychiat, 1993, 50(12): 975-990.
6.	Conte H R, Plutchik R. A circumplex model for interpersonal personality traits. J Pers Soc Psychol, 1981, 40(4): 701-711.
7.	Vinciarelli A, Mohammadi G. A survey of personality computing. IEEE T Affect Comput, 2014, 5(3): 273-291.
8.	Compton W C. Measures of mental health and a five factor theory of personality. Psychol Rep, 1998, 83(1): 371-381.
9.	Noftle E E, Shaver P R. Attachment dimensions and the big five personality traits: associations and comparative ability to predict relationship quality. J Res Pers, 2006, 40(2): 179-208.
10.	Komarraju M, Karau S J, Schmeck R R, et al. The big five personality traits, learning styles, and academic achievement. Pers Indiv Differ, 2011, 51(4): 472-477.
11.	Judge T A, Higgins C A, Thoresen C J, et al. The big five personality traits, general mental ability, and career success across the life span. Pers Psychol, 1999, 52(3): 621-652.
12.	Li W, Hu X, Long X, et al. EEG responses to emotional videos can quantitatively predict big-five personality traits. Neurocomputing, 2020, 415: 368-381.
13.	Costa P T Jr, McCrae R R. Domains and facets: hierarchical personality assessment using the revised NEO personality inventory. J Pers Assess, 1995, 64(1): 21-50.
14.	McCrae R R, Costa P T Jr. A contemplated revision of the NEO five-factor inventory. Pers Indiv Differ, 2004, 36(3): 587-596.
15.	Furnham A, Forde L, Cotter T. Personality scores and test taking style. Pers Indiv Differ, 1998, 24(1): 19-23.
16.	李文钰. 基于情绪脑电影响的大五人格测量方法研究. 北京: 清华大学, 2019.
17.	Youyou W, Kosinski M, Stillwell D. Computer-based personality judgments are more accurate than those made by humans. Proc Natl Acad Sci USA, 2015, 112(4): 1036-1040.
18.	Jacques Junior J C S, Güçlütürk Y, Pérez M, et al. First impressions: a survey on vision-based apparent personality trait analysis. IEEE T Affect Comput, 2019, 13(1): 75-95.
19.	Hoppe S, Loetscher T, Morey S A, et al. Eye movements during everyday behavior predict personality traits. Front Hum Neurosci, 2018, 12: 105.
20.	Privado J, Román F J, Saénz-Urturi C, et al. Gray and white matter correlates of the big five personality traits. Neuroscience, 2017, 349: 174-184.
21.	Coutinho J F, Sampaio A, Ferreira M, et al. Brain correlates of pro-social personality traits: a voxel-based morphometry study. Brain Imaging Behav, 2013, 7(3): 293-299.
22.	Bjørnebekk A, Fjell A M, Walhovd K B, et al. Neuronal correlates of the five factor model (FFM) of human personality: multimodal imaging in a large healthy sample. Neuroimage, 2013, 65: 194-208.
23.	DeYoung C G, Hirsh J B, Shane M S, et al. Testing predictions from personality neuroscience: brain structure and the big five. Psychol Sci, 2010, 21(6): 820-828.
24.	Riccelli R, Toschi N, Nigro S, et al. Surface-based morphometry reveals the neuroanatomical basis of the five-factor model of personality. Soc Cogn Affect Neur, 2017, 12(4): 671-684.
25.	Li W, Wu C, Hu X, et al. Quantitative personality predictions from a brief EEG recording. IEEE T Affect Comput, 2020. DOI: 10.1109/TAFFC.2020.3008775.
26.	Klados M A, Konstantinidi P, Dacosta-Aguayo R, et al. Automatic recognition of personality profiles using EEG functional connectivity during emotional processing. Brain Sci, 2020, 10(5): 278.
27.	Subramanian R, Wache J, Abadi M K, et al. ASCERTAIN: emotion and personality recognition using commercial sensors. IEEE T Affect Comput, 2016, 9(2): 147-160.
28.	Zhao G, Ge Y, Shen B, et al. Emotion analysis for personality inference from EEG signals. IEEE T Affect Comput, 2017, 9(3): 362-371.
29.	Correa J A M, Abadi M K, Sebe N, et al. Amigos: a dataset for affect, personality and mood research on individuals and groups. IEEE T Affect Comput, 2021, 12(2): 479-493.
30.	Abadi M K, Correa J A M, Wache J, et al. Inference of personality traits and affect schedule by analysis of spontaneous reactions to affective videos// 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG). Ljubljana: IEEE, 2015: 1-8.
31.	Jach H K, Feuerriegel D, Smillie L D. Decoding personality trait measures from resting EEG: an exploratory report. Cortex, 2020, 130: 158-171.
32.	Sporns O. Contributions and challenges for network models in cognitive neuroscience. Nat Neurosci, 2014, 17(5): 652-660.
33.	Mulders P, Llera A, Tendolkar I, et al. Personality profiles are associated with functional brain networks related to cognition and emotion. Sci Rep-UK, 2018, 8(1): 1-8.
34.	Niso G, Bruña R, Pereda E, et al. HERMES: towards an integrated toolbox to characterize functional and effective brain connectivity. Neuroinformatics, 2013, 11(4): 405-434.
35.	La Rocca D, Campisi P, Vegso B, et al. Human brain distinctiveness based on EEG spectral coherence connectivity. IEEE T Bio-Med Eng, 2014, 61(9): 2406-2412.
36.	Zheng W L, Zhang Y Q, Zhu J Y, et al. Transfer components between subjects for EEG-based emotion recognition// 2015 International Conference on Affective Computing and Intelligent Interaction (ACII). Xi'an: IEEE, 2015: 917-922.
37.	Li J, Qiu S, Shen Y, et al. Multisource transfer learning for cross-subject EEG emotion recognition. IEEE T Cybernetics, 2019, 50(7): 3281-3293.
38.	Jiménez-Guarneros M, Gómez-Gil P. Custom domain adaptation: a new method for cross-subject, EEG-based cognitive load recognition. IEEE Signal Proc Let, 2020, 27: 750-754.
39.	白露, 马慧, 黄宇霞, 等. 中国情绪图片系统的编制-在46名中国大学生中的试用. 中国心理卫生杂志, 2005, 19(11): 719-722.
40.	戴晓阳, 姚树桥, 蔡太生, 等. NEO个性问卷修订本在中国的应用研究. 中国心理卫生杂志, 2004, 18(3): 171-174.
41.	Liu S, Zhang D, Tong J, et al. EEG-based emotion estimation using adaptive tracking of discriminative frequency components// 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Jeju: IEEE, 2017: 2231-2234.
42.	Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Meth, 2004, 134(1): 9-21.
43.	Mognon A, Jovicich J, Bruzzone L, et al. ADJUST: an automatic EEG artifact detector based on the joint use of spatial and temporal features. Psychophysiology, 2011, 48(2): 229-240.
44.	Pion-Tonachini L, Kreutz-Delgado K, Makeig S. ICLabel: an automated electroencephalographic independent component classifier, dataset, and website. Neuroimage, 2019, 198: 181-197.
45.	Borgwardt K M, Gretton A, Rasch M J, et al. Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics, 2006, 22(14): e49-e57.
46.	Long M, Wang J, Ding G, et al. Transfer feature learning with joint distribution adaptation// Proceedings of the IEEE International Conference on Computer Vision (ICCV). Sydney: IEEE, 2013: 2200-2207.
47.	Wang J, Chen Y, Hao S, et al. Balanced distribution adaptation for transfer learning// 2017 IEEE International Conference on Data Mining (ICDM). New Orleans: IEEE, 2017: 1129-1134.
48.	Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res, 2008, 9(11): 2579-2605.
49.	Chen H, Sun S, Li J, et al. Personal-Zscore: eliminating individual difference for EEG-based cross-subject emotion recognition. IEEE T Affect Comput, 2021. DOI: 10.1109/TAFFC.2021.3137857.
50.	Delorme A, Miyakoshi M, Jung T P, et al. Grand average ERP-image plotting and statistics: a method for comparing variability in event-related single-trial EEG activities across subjects and conditions. J Neurosci Meth, 2015, 250: 3-6.
51.	Makeig S, Westerfield M, Jung T P, et al. Dynamic brain sources of visual evoked responses. Science, 2002, 295(5555): 690-694.
52.	Tran Y, Craig A, Boord P, et al. Personality traits and its association with resting regional brain activity. Int J Psychophysiol, 2006, 60(3): 215-224.
53.	Stenberg G. Personality and the EEG: arousal and emotional arousability. Pers Indiv Differ, 1992, 13(10): 1097-1113.
54.	Gale A, Edwards J, Morris P, et al. Extraversion-introversion, neuroticism-stability, and EEG indicators of positive and negative empathic mood. Pers Indiv Differ, 2001, 30(3): 449-461.
55.	He B, Astolfi L, Valdés-Sosa P A, et al. Electrophysiological brain connectivity: theory and implementation. IEEE T Bio-Med Eng, 2019, 66(7): 2115-2137.
56.	郑敬华, 郭世泽, 高梁, 等. 基于多任务学习的大五人格预测. 中国科学院大学学报, 2018, 35(4): 550-560.
57.	DeYoung C G. Higher-order factors of the big five in a multi-informant sample. J Pers Soc Psychol, 2006, 91(6): 1138-1151.
58.	Gao S, Li W, Song L J, et al. PersonalitySensing: a multi-view multi-task learning approach for personality detection based on smartphone usage// Proceedings of the 28th ACM International Conference on Multimedia. Seattle: ACM, 2020: 2862-2870.
59.	Wang Y, Yao Q, Kwok J T, et al. Generalizing from a few examples: a survey on few-shot learning. ACM Comput Surv, 2020, 53(3): 1-34.

1. 陈少华. 人格心理学. 广州: 暨南大学出版社, 2010.
2. Eysenck H J, Eysenck S B G. Manual of the Eysenck personality questionnaire. Londa: Hodder and Stoughton Educational, 1975.
3. Yang J, McCrae R R, Costa P T Jr, et al. Cross-cultural personality assessment in psychiatric populations: the NEO-PI-R in the People's Republic of China. Psychol Assess, 1999, 11(3): 359-368.
4. Ashton M C, Lee K, Perugini M, et al. A six-factor structure of personality-descriptive adjectives: solutions from psycholexical studies in seven languages. J Pers Soc Psychol, 2004, 86(2): 356-366.
5. Cloninger C R, Svrakic D M, Przybeck T R. A psychobiological model of temperament and character. Arch Gen Psychiat, 1993, 50(12): 975-990.
6. Conte H R, Plutchik R. A circumplex model for interpersonal personality traits. J Pers Soc Psychol, 1981, 40(4): 701-711.
7. Vinciarelli A, Mohammadi G. A survey of personality computing. IEEE T Affect Comput, 2014, 5(3): 273-291.
8. Compton W C. Measures of mental health and a five factor theory of personality. Psychol Rep, 1998, 83(1): 371-381.
9. Noftle E E, Shaver P R. Attachment dimensions and the big five personality traits: associations and comparative ability to predict relationship quality. J Res Pers, 2006, 40(2): 179-208.
10. Komarraju M, Karau S J, Schmeck R R, et al. The big five personality traits, learning styles, and academic achievement. Pers Indiv Differ, 2011, 51(4): 472-477.
11. Judge T A, Higgins C A, Thoresen C J, et al. The big five personality traits, general mental ability, and career success across the life span. Pers Psychol, 1999, 52(3): 621-652.
12. Li W, Hu X, Long X, et al. EEG responses to emotional videos can quantitatively predict big-five personality traits. Neurocomputing, 2020, 415: 368-381.
13. Costa P T Jr, McCrae R R. Domains and facets: hierarchical personality assessment using the revised NEO personality inventory. J Pers Assess, 1995, 64(1): 21-50.
14. McCrae R R, Costa P T Jr. A contemplated revision of the NEO five-factor inventory. Pers Indiv Differ, 2004, 36(3): 587-596.
15. Furnham A, Forde L, Cotter T. Personality scores and test taking style. Pers Indiv Differ, 1998, 24(1): 19-23.
16. 李文钰. 基于情绪脑电影响的大五人格测量方法研究. 北京: 清华大学, 2019.
17. Youyou W, Kosinski M, Stillwell D. Computer-based personality judgments are more accurate than those made by humans. Proc Natl Acad Sci USA, 2015, 112(4): 1036-1040.
18. Jacques Junior J C S, Güçlütürk Y, Pérez M, et al. First impressions: a survey on vision-based apparent personality trait analysis. IEEE T Affect Comput, 2019, 13(1): 75-95.
19. Hoppe S, Loetscher T, Morey S A, et al. Eye movements during everyday behavior predict personality traits. Front Hum Neurosci, 2018, 12: 105.
20. Privado J, Román F J, Saénz-Urturi C, et al. Gray and white matter correlates of the big five personality traits. Neuroscience, 2017, 349: 174-184.
21. Coutinho J F, Sampaio A, Ferreira M, et al. Brain correlates of pro-social personality traits: a voxel-based morphometry study. Brain Imaging Behav, 2013, 7(3): 293-299.
22. Bjørnebekk A, Fjell A M, Walhovd K B, et al. Neuronal correlates of the five factor model (FFM) of human personality: multimodal imaging in a large healthy sample. Neuroimage, 2013, 65: 194-208.
23. DeYoung C G, Hirsh J B, Shane M S, et al. Testing predictions from personality neuroscience: brain structure and the big five. Psychol Sci, 2010, 21(6): 820-828.
24. Riccelli R, Toschi N, Nigro S, et al. Surface-based morphometry reveals the neuroanatomical basis of the five-factor model of personality. Soc Cogn Affect Neur, 2017, 12(4): 671-684.
25. Li W, Wu C, Hu X, et al. Quantitative personality predictions from a brief EEG recording. IEEE T Affect Comput, 2020. DOI: 10.1109/TAFFC.2020.3008775.
26. Klados M A, Konstantinidi P, Dacosta-Aguayo R, et al. Automatic recognition of personality profiles using EEG functional connectivity during emotional processing. Brain Sci, 2020, 10(5): 278.
27. Subramanian R, Wache J, Abadi M K, et al. ASCERTAIN: emotion and personality recognition using commercial sensors. IEEE T Affect Comput, 2016, 9(2): 147-160.
28. Zhao G, Ge Y, Shen B, et al. Emotion analysis for personality inference from EEG signals. IEEE T Affect Comput, 2017, 9(3): 362-371.
29. Correa J A M, Abadi M K, Sebe N, et al. Amigos: a dataset for affect, personality and mood research on individuals and groups. IEEE T Affect Comput, 2021, 12(2): 479-493.
30. Abadi M K, Correa J A M, Wache J, et al. Inference of personality traits and affect schedule by analysis of spontaneous reactions to affective videos// 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG). Ljubljana: IEEE, 2015: 1-8.
31. Jach H K, Feuerriegel D, Smillie L D. Decoding personality trait measures from resting EEG: an exploratory report. Cortex, 2020, 130: 158-171.
32. Sporns O. Contributions and challenges for network models in cognitive neuroscience. Nat Neurosci, 2014, 17(5): 652-660.
33. Mulders P, Llera A, Tendolkar I, et al. Personality profiles are associated with functional brain networks related to cognition and emotion. Sci Rep-UK, 2018, 8(1): 1-8.
34. Niso G, Bruña R, Pereda E, et al. HERMES: towards an integrated toolbox to characterize functional and effective brain connectivity. Neuroinformatics, 2013, 11(4): 405-434.
35. La Rocca D, Campisi P, Vegso B, et al. Human brain distinctiveness based on EEG spectral coherence connectivity. IEEE T Bio-Med Eng, 2014, 61(9): 2406-2412.
36. Zheng W L, Zhang Y Q, Zhu J Y, et al. Transfer components between subjects for EEG-based emotion recognition// 2015 International Conference on Affective Computing and Intelligent Interaction (ACII). Xi'an: IEEE, 2015: 917-922.
37. Li J, Qiu S, Shen Y, et al. Multisource transfer learning for cross-subject EEG emotion recognition. IEEE T Cybernetics, 2019, 50(7): 3281-3293.
38. Jiménez-Guarneros M, Gómez-Gil P. Custom domain adaptation: a new method for cross-subject, EEG-based cognitive load recognition. IEEE Signal Proc Let, 2020, 27: 750-754.
39. 白露, 马慧, 黄宇霞, 等. 中国情绪图片系统的编制-在46名中国大学生中的试用. 中国心理卫生杂志, 2005, 19(11): 719-722.
40. 戴晓阳, 姚树桥, 蔡太生, 等. NEO个性问卷修订本在中国的应用研究. 中国心理卫生杂志, 2004, 18(3): 171-174.
41. Liu S, Zhang D, Tong J, et al. EEG-based emotion estimation using adaptive tracking of discriminative frequency components// 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Jeju: IEEE, 2017: 2231-2234.
42. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Meth, 2004, 134(1): 9-21.
43. Mognon A, Jovicich J, Bruzzone L, et al. ADJUST: an automatic EEG artifact detector based on the joint use of spatial and temporal features. Psychophysiology, 2011, 48(2): 229-240.
44. Pion-Tonachini L, Kreutz-Delgado K, Makeig S. ICLabel: an automated electroencephalographic independent component classifier, dataset, and website. Neuroimage, 2019, 198: 181-197.
45. Borgwardt K M, Gretton A, Rasch M J, et al. Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics, 2006, 22(14): e49-e57.
46. Long M, Wang J, Ding G, et al. Transfer feature learning with joint distribution adaptation// Proceedings of the IEEE International Conference on Computer Vision (ICCV). Sydney: IEEE, 2013: 2200-2207.
47. Wang J, Chen Y, Hao S, et al. Balanced distribution adaptation for transfer learning// 2017 IEEE International Conference on Data Mining (ICDM). New Orleans: IEEE, 2017: 1129-1134.
48. Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res, 2008, 9(11): 2579-2605.
49. Chen H, Sun S, Li J, et al. Personal-Zscore: eliminating individual difference for EEG-based cross-subject emotion recognition. IEEE T Affect Comput, 2021. DOI: 10.1109/TAFFC.2021.3137857.
50. Delorme A, Miyakoshi M, Jung T P, et al. Grand average ERP-image plotting and statistics: a method for comparing variability in event-related single-trial EEG activities across subjects and conditions. J Neurosci Meth, 2015, 250: 3-6.
51. Makeig S, Westerfield M, Jung T P, et al. Dynamic brain sources of visual evoked responses. Science, 2002, 295(5555): 690-694.
52. Tran Y, Craig A, Boord P, et al. Personality traits and its association with resting regional brain activity. Int J Psychophysiol, 2006, 60(3): 215-224.
53. Stenberg G. Personality and the EEG: arousal and emotional arousability. Pers Indiv Differ, 1992, 13(10): 1097-1113.
54. Gale A, Edwards J, Morris P, et al. Extraversion-introversion, neuroticism-stability, and EEG indicators of positive and negative empathic mood. Pers Indiv Differ, 2001, 30(3): 449-461.
55. He B, Astolfi L, Valdés-Sosa P A, et al. Electrophysiological brain connectivity: theory and implementation. IEEE T Bio-Med Eng, 2019, 66(7): 2115-2137.
56. 郑敬华, 郭世泽, 高梁, 等. 基于多任务学习的大五人格预测. 中国科学院大学学报, 2018, 35(4): 550-560.
57. DeYoung C G. Higher-order factors of the big five in a multi-informant sample. J Pers Soc Psychol, 2006, 91(6): 1138-1151.
58. Gao S, Li W, Song L J, et al. PersonalitySensing: a multi-view multi-task learning approach for personality detection based on smartphone usage// Proceedings of the 28th ACM International Conference on Multimedia. Seattle: ACM, 2020: 2862-2870.
59. Wang Y, Yao Q, Kwok J T, et al. Generalizing from a few examples: a survey on few-shot learning. ACM Comput Surv, 2020, 53(3): 1-34.

Journal of Biomedical Engineering

Cross subject personality assessment based on electroencephalogram functional connectivity and domain adaptation

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