Implicit affective profiling of subjects based on physiological data coupling

References

• Damasio, A Descartes’ error: Emotion, reason and the human brain [Internet]. Random House; 2008. Available from: http://books.google.ch/books?hl=en&lr=&id=MRY3hmYc1W8C&oi=fnd&pg=PT1&dq=Descartes%E2%80%99±error:±Emotion,±Reason±and±the±Human±Brain&ots=RbU_6e4o-x&sig=RiSTD08JYF3laBB06fn3K3fzLmQ
   Google Scholar
• De Martino B, Kumaran D, Seymour B, Dolan RJ. Frames, biases, and rational decision-making in the human brain. Science. 2006;313:684–687.10.1126/science.1128356
   Google Scholar
• Seymour B, Dolan R. Emotion, decision making, and the amygdala. Neuron. 2008;58:662–671.10.1016/j.neuron.2008.05.020
   Google Scholar
• Loewenstein G, Lerner JS. The role of affect in decision making. Handb Affect Sci. 2003;619:3.
   Google Scholar
• Damasio AR, Everitt BJ, Bishop D. The somatic marker hypothesis and the possible functions of the prefrontal cortex [and discussion]. Philos Trans R Soc Lond B Biol Sci. 1996;351:1413–1420.10.1098/rstb.1996.0125
   Google Scholar
• Soleymani M, Lichtenauer J, Pun T, Pantic M. A multimodal database for affect recognition and implicit tagging. Affect Comput IEEE Trans On. 2012;3:42–55.10.1109/T-AFFC.2011.25
   Google Scholar
• Koelstra S, Muhl C, Soleymani M, Lee JS, Yazdani A, Ebrahimi T, et al. Deap: a database for emotion analysis; using physiological signals. Affect Comput IEEE Trans On. 2012;3:18–31.10.1109/T-AFFC.2011.15
   Google Scholar
• Soleymani M, Pantic M, Pun T. Multimodal emotion recognition in response to videos. Affect Comput IEEE Trans On. 2012;3:211–223.10.1109/T-AFFC.2011.37
   Google Scholar
• Pantic M, Vinciarelli A. Implicit human-centered tagging [Social Sciences]. ignal Process Mag IEEE. 2009;26:173–180.10.1109/MSP.2009.934186
   Google Scholar
• Soleymani, M, Pantic, M. Human-centered implicit tagging: overview and perspectives. Systems, Man, and Cybernetics (SMC). 2012 IEEE International Conference on. IEEE; 2012. p. 3304–3309.
   Google Scholar
• Ekman P. An argument for basic emotions. Cogn Emot. 1992;6:169–200.10.1080/02699939208411068
   Google Scholar
• Ekman P, Friesen WV, O'Sullivan M, Chan A, Diacoyanni-Tarlatzis I, Heider K, Krause R, LeCompte WA, Pitcairn T, Ricci-Bitti PE, Scherer K, Tomita M, Tzavaras A. Universals and cultural differences in the judgments of facial expressions of emotion. J Pers Soc Psychol. 1987;53:712.10.1037/0022-3514.53.4.712
   Google Scholar
• Picard, RW. Affective computing. MIT press; 2000.
   Google Scholar
• Gross JJ, John OP. Individual differences in two emotion regulation processes: implications for affect, relationships, and well-being. J Pers Soc Psychol. 2003;85:348.10.1037/0022-3514.85.2.348
   Google Scholar
• Grandey AA. Emotional regulation in the workplace: A new way to conceptualize emotional labor. J Occup Health Psychol. 2000;5:95.10.1037/1076-8998.5.1.95
   Google Scholar
• Hochschild AR. Emotion work, feeling rules, and social structure. Am J Sociol. 1979;551–575.10.1086/ajs.1979.85.issue-3
   Google Scholar
• Ekman P, Friesen WV. Constants across cultures in the face and emotion. J Pers Soc Psychol. 1971;17:124.10.1037/h0030377
   Google Scholar
• Koelstra S, Yazdani A, Soleymani M, Mühl C, Lee J-S, Nijholt A, et al. Single trial classification of EEG and peripheral physiological signals for recognition of emotions induced by music videos. Brain informatics: Springer; 2010.
   Google Scholar
• Lisetti CL, Nasoz F. Using noninvasive wearable computers to recognize human emotions from physiological signals. EURASIP J Appl Signal Process. 2004;2004:1672–1687.10.1155/S1110865704406192
   Google Scholar
• Picard, W, Healey, JA. Wearable and automotive systems for affect recognition from physiology. 2000; Available from: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.1519
   Google Scholar
• Kolodyazhniy V, Kreibig SD, Gross JJ, Roth WT, Wilhelm FH. An affective computing approach to physiological emotion specificity: toward subject-independent and stimulus-independent classification of film-induced emotions. Psychophysiology. 2011;48:908–922.10.1111/psyp.2011.48.issue-7
   Google Scholar
• Chanel G, Rebetez C, Bétrancourt M, Pun T. Emotion assessment from physiological signals for adaptation of game difficulty. Syst Man Cybern Part Syst Hum IEEE Trans On. 2011;41:1052–1063.10.1109/TSMCA.2011.2116000
   Google Scholar
• Gunes, H, Schuller, B. Categorical and dimensional affect analysis in continuous input: current trends and future directions. Image Vis Comput [Internet]. 2012; Available from: http://www.sciencedirect.com/science/article/pii/S0262885612001084
   Google Scholar
• Gunes, H, Piccardi M, Pantic M. From the lab to the real world: affect recognition using multiple cues and modalities. 2008; Available from: http://doc.utwente.nl/65266/1/pantic_from_the_lab_book_chapter.pdf
   Google Scholar
• Kim J, André E. Emotion recognition based on physiological changes in music listening. Pattern Anal Mach Intell IEEE Trans On. 2008;30:2067–2083.
   Google Scholar
• Lang PJ, Greenwald MK, Bradley MM, Hamm AO. Looking at pictures: affective, facial, visceral, and behavioral reactions. Psychophysiology. 1993;30:261–273.10.1111/psyp.1993.30.issue-3
   Google Scholar
• Chanel G, Kierkels JJ, Soleymani M, Pun T. Short-term emotion assessment in a recall paradigm. Int J Hum-Comput Stud. 2009;67:607–627.10.1016/j.ijhcs.2009.03.005
   Google Scholar
• Calvo RA, D’Mello S. Affect detection: an interdisciplinary review of models, methods, and their applications. Affect Comput IEEE Trans On. 2010;1:18–37.10.1109/T-AFFC.2010.1
   Google Scholar
• Kelso, JA. Dynamic Patterns: The Self Organization of Brain and Behaviour [Internet]. The MIT Press; 1995. Available from: http://books.google.ch/books?hl=en&lr=&id=zpjejjytkiIC&oi=fnd&pg=PR9&dq=Dynamic±patterns:±The±self-organization±of±brain±and±behavior.&ots=-alDWEd4dq&sig=T3lf5Ll0HvqLd-XFZht7Q_ilLHE
   Google Scholar
• Scherer KR. Appraisal considered as a process of multilevel sequential checking. Apprais Process Emot Theory Methods Res. 2001;92:120.
   Google Scholar
• Scherer KR. Neuroscience projections to current debates in emotion psychology. Cogn Emot. 1993;7:1–41.10.1080/02699939308409174
   Google Scholar
• Sander D, Grandjean D, Scherer KR. A systems approach to appraisal mechanisms in emotion. Neural Netw. 2005;18:317–352.10.1016/j.neunet.2005.03.001
   Google Scholar
• Grandjean D, Sander D, Scherer KR. Conscious emotional experience emerges as a function of multilevel, appraisal-driven response synchronization. Conscious Cogn. 2008;17:484–495.10.1016/j.concog.2008.03.019
   Google Scholar
• Frackowiak, RS, Friston, KJ, Frith, CD, Dolan, RJ, Price, CJ, Zeki, S, et al. Human brain function [Internet]. Academic Press; 2004. Available from: http://books.google.ch/books?hl=en&lr=&id=AoWD2S8759kC&oi=fnd&pg=PA1&dq=Human±brain±function&ots=EVAoau8V6-&sig=FsBVPPM35MhRlteICUU9l9slQAY
   Google Scholar
• Valenza G, Lanata A, Scilingo EP. Oscillations of heart rate and respiration synchronize during affective visual stimulation. Inf Technol Biomed IEEE Trans On. 2012;16:683–690.10.1109/TITB.2012.2197632
   Google Scholar
• Valenza G, Lanata A, Scilingo EP. The role of nonlinear dynamics in affective valence and arousal recognition. Affect Comput IEEE Trans On. 2012;3:237–249.10.1109/T-AFFC.2011.30
   Google Scholar
• Bennett M, Schatz MF, Rockwood H, Wiesenfeld K. Huygens’s clocks. Proc Math Phys: Eng Sci; 2002. p. 563–579.
   Google Scholar
• Buck J. Synchronous rhythmic flashing of fireflies. II. Q Rev Biol. 1988;265–289.10.1086/qrb.1988.63.issue-3
   Google Scholar
• McClintock, MK. Menstrual synchrony and suppression. Nature [Internet]. 1971; Available from: http://psycnet.apa.org/psycinfo/1971-24368-001
   Google Scholar
• Schäfer C, Rosenblum MG, Abel H-H, Kurths J. Synchronization in the human cardiorespiratory system. Phys Rev E. 1999;60:857.10.1103/PhysRevE.60.857
   Google Scholar
• Pfurtscheller G, Daly I, Bauernfeind G, Müller-Putz GR. Coupling between Intrinsic Prefrontal HbO2 and Central EEG Beta Power Oscillations in the Resting Brain. PloS One. 2012;7:e43640.10.1371/journal.pone.0043640
   Google Scholar
• Brandenberger G, Ehrhart J, Piquard F, Simon C. Inverse coupling between ultradian oscillations in delta wave activity and heart rate variability during sleep. Clin Neurophysiol. 2001;112:992–996.10.1016/S1388-2457(01)00507-7
   Google Scholar
• Dumont M, Jurysta F, Lanquart J-P, Migeotte P-F, Van De Borne P, Linkowski P. Interdependency between heart rate variability and sleep EEG: linear/non-linear? Clin Neurophysiol. 2004;115:2031–2040.10.1016/j.clinph.2004.04.007
   Google Scholar
• Lambertz M, Langhorst P. Simultaneous changes of rhythmic organization in brainstem neurons, respiration, cardiovascular system and EEG between 0.05 Hz and 0.5 Hz. J Auton Nerv Syst. 1998;68:58–77.10.1016/S0165-1838(97)00126-4
   Google Scholar
• Pfurtscheller K, Bauernfeind G, Müller-Putz GR, Urlesberger B, Müller W, Pfurtscheller G. Correlation between EEG burst-to-burst intervals and HR acceleration in preterm infants. Neurosci Lett. 2008;437:103–106.10.1016/j.neulet.2008.03.079
   Google Scholar
• Rosenblum M, Pikovsky A, Kurths J, Schäfer C, Tass PA. Phase synchronization: from theory to data analysis. Handb Biol Phys. 2001;4:279–321.10.1016/S1383-8121(01)80012-9
   Google Scholar
• Dawson ME, Schell AM, Filion DL. 7 The Electrodermal System. Handb Psychophysiol. 2007;159.
   Google Scholar
• Nagai Y, Critchley HD, Featherstone E, Trimble MR, Dolan RJ. Activity in ventromedial prefrontal cortex covaries with sympathetic skin conductance level: a physiological account of a “default mode” of brain function. Neuroimage. 2004;22:243–251.10.1016/j.neuroimage.2004.01.019
   Google Scholar
• Critchley HD, Melmed RN, Featherstone E, Mathias CJ, Dolan RJ. Volitional control of autonomic arousal: a functional magnetic resonance study. Neuroimage. 2002;16:909–919.10.1006/nimg.2002.1147
   Google Scholar
• Davidson RJ. What does the prefrontal cortex “do” in affect: perspectives on frontal EEG asymmetry research. Biol Psychol. 2004;67:219–234.10.1016/j.biopsycho.2004.03.008
   Google Scholar
• Coan JA, Allen JJ. Frontal EEG asymmetry as a moderator and mediator of emotion. Biol Psychol. 2004;67:7–50.10.1016/j.biopsycho.2004.03.002
   Google Scholar
• Lin Y-P, Wang C-H, Jung T-P, Wu T-L, Jeng S-K, Duann J-R, et al. EEG-based emotion recognition in music listening. Biomed Eng IEEE Trans On. 2010;57:1798–1806.
   Google Scholar
• Kroupi E, Yazdani A, Vesin J-M, Ebrahimi T. Multivariate spectral analysis for identifying the brain activations during olfactory perception. Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE [Internet]. 2012. p. 6172–6175. Available from: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6347403
   Google Scholar
• Kroupi E, Yazdani A, Ebrahimi T EEG correlates of different emotional states elicited during watching music videos. Affective Computing and Intelligent Interaction [Internet]. Springer; 2011. p. 457–466. Available from: http://link.springer.com/chapter/10.1007/978-3-642-24571-8_58
   Google Scholar
• Yazdani A, Kroupi E, Vesin J-M, Ebrahimi T Electroencephalogram alterations during perception of pleasant and unpleasant odors. Quality of Multimedia Experience (QoMEX), 2012 Fourth International Workshop on [Internet]. 2012. p. 272–277. Available from: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=626386010.1109/QoMEX.2012.6263860
   Google Scholar
• Hadjidimitriou SK, Hadjileontiadis LJ. Towards an EEG-based recognition of music liking using time-frequency analysis. 2012; Available from: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6313893
   Google Scholar
• Petrantonakis PC, Hadjileontiadis LJ. Emotion recognition from EEG using higher order crossings. Inf Technol Biomed IEEE Trans On. 2010;14:186–197.10.1109/TITB.2009.2034649
   Google Scholar
• Bashashati A, Fatourechi M, Ward RK, Birch GE. A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals. J Neural Eng. 2007;4:R32.10.1088/1741-2560/4/2/R03
   Google Scholar
• Lotte F, Congedo M, Lécuyer A, Lamarche F, Arnaldi B. A review of classification algorithms for EEG-based brain–computer interfaces. J Neural Eng [Internet]. 2007;4. Available from: http://hal.archives-ouvertes.fr/docs/00/13/49/50/PDF/article.pdf
   Google Scholar
• Tort AB, Komorowski R, Eichenbaum H, Kopell N. Measuring phase-amplitude coupling between neuronal oscillations of different frequencies. J Neurophysiol. 2010;104:1195–1210.10.1152/jn.00106.2010
   Google Scholar
• Gabor D Theory of communication. Part 1: The analysis of information. Electr Eng-Part III Radio Commun Eng J Inst Of. 1946;93:429–441.
   Google Scholar
• Fujisaka H, Yamada T. Stability theory of synchronized motion in coupled-oscillator systems. Prog Theor Phys. 1983;69:32–47.10.1143/PTP.69.32
   Google Scholar
• Kullback S, Leibler RA. On information and sufficiency. Ann Math Stat. 1951;22:79–86.10.1214/aoms/1177729694
   Google Scholar
• Efron B, Tibshirani R. An introduction to the bootstrap. CRC Press; 1993.10.1007/978-1-4899-4541-9
   Google Scholar
• Stouffer SA, Lumsdaine AA, Lumsdaine MH, Williams RMJr, Smith MB, Janis IL, et al. The American soldier: combat and its aftermath. (Studies in social psychology in World War II, Vol. 2.). 1949; Available from: http://psycnet.apa.org/psycinfo/1950-00791-000
   Google Scholar
• Whitlock MC. Combining probability from independent tests: the weighted Z-method is superior to Fisher’s approach. J Evol Biol. 2005;18:1368–1373.10.1111/(ISSN)1420-9101
   Google Scholar
• Hartung J, Knapp G, Sinha BK. Statistical meta-analysis with applications [Internet]. John Wiley & Sons; 2011. Available from: http://books.google.ch/books?hl=en&lr=&id=JEoNB_2NONQC&oi=fnd&pg=PT9&dq=Statistical±Meta-Analysis±with±Application&ots=eGoMZiIHBI&sig=4CpTJe_n0IAOrxVdFeRsJfHQToE
   Google Scholar
• Russell JA, Lewicka M, Niit T. A cross-cultural study of a circumplex model of affect. J Pers Soc Psychol. 1989;57:848.10.1037/0022-3514.57.5.848
   Google Scholar
• Spearman C. The proof and measurement of association between two things. Am J Psychol. 1904;15:72–101.10.2307/1412159
   Google Scholar
• LeDoux J. The emotional brain: the mysterious underpinnings of emotional life. SimonandSchuster. com; 1998.
   Google Scholar
• Morris JS, Friston KJ, Büchel C, Frith CD, Young AW, Calder AJ, et al. A neuromodulatory role for the human amygdala in processing emotional facial expressions. Brain. 1998;121:47–57.10.1093/brain/121.1.47
   Google Scholar
• Rotshtein P, Malach R, Hadar U, Graif M, Hendler T. Feeling or features: different sensitivity to emotion in high-order visual cortex and amygdala. Neuron. 2001;32:747–757.10.1016/S0896-6273(01)00513-X
   Google Scholar
• Morris JS, Friston KJ, Dolan RJ. Experience–dependent modulation of tonotopic neural responses in human auditory cortex. Proc R Soc Lond B Biol Sci. 1998;265:649–657.10.1098/rspb.1998.0343
   Google Scholar
• Fredrikson M, Furmark T, Olsson MT, Fischer H, Andersson J. Functional neuroanatomical correlates of electrodermal activity: a positron emission tomographic study. Psychophysiology. 1998;35:179–185.10.1111/psyp.1998.35.issue-2
   Google Scholar
• Chua P, Krams M, Toni I, Passingham R, Dolan R. A functional anatomy of anticipatory anxiety. Neuroimage. 1999;9:563–571.10.1006/nimg.1999.0407
   Google Scholar
• Bos DO. EEG-based emotion recognition. Influ Vis Audit Stimuli. 2006;1–17.
   Google Scholar
• Baumgartner T, Esslen M, Jäncke L. From emotion perception to emotion experience: Emotions evoked by pictures and classical music. Int J Psychophysiol. 2006;60:34–43.10.1016/j.ijpsycho.2005.04.007
   Google Scholar