Electrophysiological representations of multivariate human emotion experience

References

• Adler, J. M., & Hershfield, H. E. (2012). Mixed emotional experience is associated with and precedes improvements in psychological well-being. PLoS One, 7(4), e35633. https://doi.org/10.1371/journal.pone.0035633
   PubMed Web of Science ®Google Scholar
• Adolphs, R. (2002). Neural systems for recognizing emotion. Current Opinion in Neurobiology, 12(2), 169–177. https://doi.org/10.1016/S0959-4388(02)00301-X
   PubMed Web of Science ®Google Scholar
• Ding, Y., Robinson, N., Zhang, S., Zeng, Q., & Guan, C. (2023). Tsception: Capturing temporal dynamics and spatial asymmetry from EEG for emotion recognition. IEEE Transactions on Affective Computing, 14(3), 2238–2250. https://doi.org/10.1109/TAFFC.2022.3169001
   Web of Science ®Google Scholar
• Ellsworth, P. C., & Smith, C. A. (1988). From appraisal to emotion: Differences among unpleasant feelings. Motivation and Emotion, 12(3), 271–302. https://doi.org/10.1007/BF00993115
   Web of Science ®Google Scholar
• Hamann, S. (2012). Mapping discrete and dimensional emotions onto the brain: Controversies and consensus. Trends in Cognitive Sciences, 16(9), 458–466. https://doi.org/10.1016/j.tics.2012.07.006
   PubMed Web of Science ®Google Scholar
• Hoemann, K., Khan, Z., Kamona, N., Dy, J., Barrett, L. F., & Quigley, K. S. (2021). Investigating the relationship between emotional granularity and cardiorespiratory physiological activity in daily life. Psychophysiology, 58(6), e13818. https://doi.org/10.1111/psyp.13818
   PubMed Web of Science ®Google Scholar
• Hu, X., Wang, F., & Zhang, D. (2022). Similar brains blend emotion in similar ways: Neural representations of individual difference in emotion profiles. Neuroimage, 247, 118819. https://doi.org/10.1016/j.neuroimage.2021.118819
   PubMed Web of Science ®Google Scholar
• Junghöfer, M., Bradley, M. M., Elbert, T. R., & Lang, P. J. (2001). Fleeting images: A new look at early emotion discrimination. Psychophysiology, 38(2), 175–178. https://doi.org/10.1111/1469-8986.3820175
   PubMed Web of Science ®Google Scholar
• Klonsky, E. D., Victor, S. E., Hibbert, A. S., & Hajcak, G. (2019). The multidimensional emotion questionnaire (MEQ): Rationale and initial psychometric properties. Journal of Psychopathology and Behavioral Assessment, 41(3), 409–424. https://doi.org/10.1007/s10862-019-09741-2
   Web of Science ®Google Scholar
• Koelstra, S., Muhl, C., Soleymani, M., Lee, J. S., Yazdani, A., Ebrahimi, T., … Patras, I. (2011). Deap: A database for emotion analysis; using physiological signals. IEEE Transactions on Affective Computing, 3(1), 18–31. https://doi.org/10.1109/T-AFFC.2011.15
   Web of Science ®Google Scholar
• Kriegeskorte, N., Mur, M., & Bandettini, P. A. (2008). Representational similarity analysis-connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 4.
   Google Scholar
• Larsen, J. T., & McGraw, A. P. (2014). The case for mixed emotions. Social and Personality Psychology Compass, 8(6), 263–274. https://doi.org/10.1111/spc3.12108
   Google Scholar
• Larsen, J. T., McGraw, A. P., & Cacioppo, J. T. (2001). Can people feel happy and sad at the same time? Journal of Personality and Social Psychology, 81(4), 684. https://doi.org/10.1037/0022-3514.81.4.684
   PubMed Web of Science ®Google Scholar
• Larsen, J. T., To, Y. M., & Fireman, G. (2007). Children’s understanding and experience of mixed emotions. Psychological Science, 18(2), 186–191. https://doi.org/10.1111/j.1467-9280.2007.01870.x
   PubMed Web of Science ®Google Scholar
• Martinent, G., Campo, M., & Ferrand, C. (2012). A descriptive study of emotional process during competition: Nature, frequency, direction, duration and co-occurrence of discrete emotions. Psychology of Sport and Exercise, 13(2), 142–151. https://doi.org/10.1016/j.psychsport.2011.10.006
   Web of Science ®Google Scholar
• Miyamoto, Y., Uchida, Y., & Ellsworth, P. C. (2010). Culture and mixed emotions: Co-occurrence of positive and negative emotions in Japan and the United States. Emotion, 10(3), 404. https://doi.org/10.1037/a0018430
   PubMed Web of Science ®Google Scholar
• Nummenmaa, L., Glerean, E., Viinikainen, M., Jääskeläinen, I. P., Hari, R., & Sams, M. (2012). Emotions promote social interaction by synchronizing brain activity across individuals. Proceedings of the National Academy of Sciences, 109(24), 9599–9604. https://doi.org/10.1073/pnas.1206095109
   PubMed Web of Science ®Google Scholar
• Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J. M. (2011). FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Computational Intelligence and Neuroscience, 2011(1), 1–9. https://doi.org/10.1155/2011/156869
   Web of Science ®Google Scholar
• Popal, H., Wang, Y., & Olson, I. R. (2019). A guide to representational similarity analysis for social neuroscience. Social Cognitive and Affective Neuroscience, 14(11), 1243–1253. https://doi.org/10.1093/scan/nsz099
   PubMed Web of Science ®Google Scholar
• Quoidbach, J., Gruber, J., Mikolajczak, M., Kogan, A., Kotsou, I., & Norton, M. I. (2014). Emodiversity and the emotional ecosystem. Journal of Experimental Psychology: General, 143(6), 2057. https://doi.org/10.1037/a0038025
   PubMed Web of Science ®Google Scholar
• Scherer, K. R. (1998, August). Analyzing emotion blends. In Proceedings of the Xth Conference of the International Society for Research on Emotions (pp. 142–148). ISRE Publications.
   Google Scholar
• Seghier, M. L., & Price, C. J. (2016). Visualising inter-subject variability in fMRI using threshold-weighted overlap maps. Scientific reports, 6(1), 1–13. https://doi.org/10.1038/srep20170
   PubMedGoogle Scholar
• Seghier, M. L., & Price, C. J. (2018). Interpreting and utilising intersubject variability in brain function. Trends in Cognitive Sciences, 22(6), 517–530. https://doi.org/10.1016/j.tics.2018.03.003
   PubMed Web of Science ®Google Scholar
• Sharma, K., Castellini, C., van den Broek, E. L., Albu-Schaeffer, A., & Schwenker, F. (2019). A dataset of continuous affect annotations and physiological signals for emotion analysis. Scientific Data, 6(1), 196. https://doi.org/10.1038/s41597-019-0209-0
   PubMedGoogle Scholar
• Storey, J. D. (2002). A direct approach to false discovery rates. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(3), 479–498. https://doi.org/10.1111/1467-9868.00346
   Web of Science ®Google Scholar
• Tugade, M. M., Fredrickson, B. L., & Feldman Barrett, L. (2004). Psychological resilience and positive emotional granularity: Examining the benefits of positive emotions on coping and health. Journal of Personality, 72(6), 1161–1190. https://doi.org/10.1111/j.1467-6494.2004.00294.x
   PubMed Web of Science ®Google Scholar
• Wang, X. W., Nie, D., & Lu, B. L. (2011). EEG-based emotion recognition using frequency domain features and support vector machines. In Neural Information Processing: 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part I 18 (pp. 734-743). Springer Berlin Heidelberg.
   Google Scholar
• Zhang, G., Yu, M., Liu, Y. J., Zhao, G., Zhang, D., & Zheng, W. (2023). SparseDGCNN: Recognizing emotion from multichannel EEG signals. IEEE Transactions on Affective Computing, 14(1), 537–548. https://doi.org/10.1109/TAFFC.2021.3051332
   Web of Science ®Google Scholar
• Zheng, W. L., Zhu, J. Y., & Lu, B. L. (2017). Identifying stable patterns over time for emotion recognition from EEG. IEEE Transactions on Affective Computing, 10(3), 417–429. https://doi.org/10.1109/TAFFC.2017.2712143
   Web of Science ®Google Scholar
• Zinner, L. R., Brodish, A. B., Devine, P. G., & Harmon-Jones, E. (2008). Anger and asymmetrical frontal cortical activity: Evidence for an anger–withdrawal relationship. Cognition & Emotion, 22(6), 1081–1093. https://doi.org/10.1080/02699930701622961
   PubMed Web of Science ®Google Scholar