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A review of emotion recognition using physiological signals

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Shu, Lin, Xie, Jinyan, Yang, Mingyue, Li, Ziyi, Li, Zhenqi, Liao, Dan, Xu, Xiangmin, Yang, Xinyi·2018·Sensors
DOI 10.3390/s18072074PMC PMC6069143
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Abstract

Emotion recognition based on physiological signals has been a hot topic and applied in many areas such as safe driving, health care and social security. In this paper, we present a comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets, features, classifiers, and the whole framework for emotion recognition based on the physiological signals. A summary and comparation among the recent studies has been conducted, which reveals the current existing problems and the future work has been discussed.

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  • Marechal et al. (2019) show that affective state can be extracted both from the user's pose, physiological features like blood pressure, and from facial features.

    “In a survey on multimodal approaches for emotion detection, Marechal et al. (2019) show that affective state can be extracted both from the user's pose (Zacharatos et al., 2014), physiological features like blood pressure (Shu et al., 2018), and from facial features (Ekman, 1993).”

    From Modeling Feedback in Interaction With Conversational Agents—A Review (2022) · cites this paper as Shu et al., 2018

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  1. [1]De Nadai S. D’Incà M. Parodi F. Benza M. Trotta A. Zero E. Zero L. Sacile R. Enhancing safety of transport by road by on-line monitoring of driver emotions Proceedings of the 2016 11th System of Systems Engineering Conference (SoSE) Kongsberg, Norway 12–16 June 2016 1 4
  2. [2]Guo R. Li S. He L. Gao W. Qi H. Owens G. Pervasive and unobtrusive emotion sensing for human mental health Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare Venice, Italy 5–8 May 2013 436 439
  3. [3]Verschuere B. Crombez G. Koster E. Uzieblo K. Psychopathy and Physiological Detection of Concealed Information: A review Psychol. Belg. 2006 46 99 116 10.5334/pb-46-1-2-99 · DOI
  4. [4]Zhang Y.D. Yang Z.J. Lu H.M. Zhou X.X. Phillips P. Liu Q.M. Wang S.H. Facial Emotion Recognition based on Biorthogonal Wavelet Entropy, Fuzzy Support Vector Machine, and Stratified Cross Validation IEEE Access 2016 4 8375 8385 10.1109/ACCESS.2016.2628407 · DOI
  5. [6]Cannon W.B. The James-Lange theory of emotions: A critical examination and an alternative theory Am. J. Psychol. 1927 39 106 124 10.2307/1415404 3322057 · DOI · Paper page
  6. [8]Plutchik R. The Nature of Emotions Am. Sci. 2001 89 344 350 10.1511/2001.4.344 · DOI
  7. [9]Izard C.E. Basic Emotions, Natural Kinds, Emotion Schemas, and a New Paradigm Perspect. Psychol. Sci. 2007 2 260 280 10.1111/j.1745-6916.2007.00044.x 26151969 · DOI · Paper page
  8. [10]Izard C.E. Emotion theory and research: Highlights, unanswered questions, and emerging issues Annu. Rev. Psychol. 2008 60 1 25 10.1146/annurev.psych.60.110707.163539 18729725 PMC2723854 · DOI
  9. [11]Lang P.J. The emotion probe: Studies of motivation and attention Am. Psychol. 1995 50 372 385 10.1037/0003-066X.50.5.372 7762889 · DOI
  10. [12]Mehrabian A. Comparison of the PAD and PANAS as models for describing emotions and for differentiating anxiety from depression J. Psychopathol. Behav. Assess. 1997 19 331 357 10.1007/BF02229025 · DOI
  11. [15]Nasoz F. Alvarez K. Emotion Recognition from Physiological Signals for Presence Technologies Int. J. Cogn. Technol. Work 2003 6 4 14 10.1007/s10111-003-0143-x · DOI
  12. [16]Koelstra S. Muhl C. Soleymani M. Lee J.S. Yazdani A. Ebrahimi T. Pun T. Nijholt A. Patras I. DEAP: A Database for Emotion Analysis; Using Physiological Signals IEEE Trans. Affect. Comput. 2012 3 18 31 10.1109/T-AFFC.2011.15 · DOI · Paper page
  13. [17]Zhang W. Shu L. Xu X. Liao D. Affective Virtual Reality System (AVRS): Design and Ratings of Affective VR Scences Proceedings of the International Conference on Virtua lReality and Visualization Zhengzhou, China 21–22 October 2017
  14. [18]Merkx P.P. Truong K.P. Neerincx M.A. Inducing and measuring emotion through a multiplayer first-person shooter computer game Proceedings of the Computer Games Workshop Melbourne, Australia 20 August 2007
  15. [19]Chanel G. Kierkels J.J. 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 · DOI · Paper page
  16. [21]Wu S. Xu X. Shu L. Hu B. Estimation of valence of emotion using two frontal EEG channels Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine Kansas City, MO, USA 13–16 November 2017 1127 1130
  17. [22]Lan Z. Sourina O. Wang L. Liu Y. Real-time EEG-based emotion monitoring using stable features Vis. Comput. 2016 32 347 358 10.1007/s00371-015-1183-y · DOI
  18. [23]Qiao R. Qing C. Zhang T. Xing X. Xu X. A novel deep-learning based framework for multi-subject emotion recognition Proceedings of the International Conference on Information, Cybernetics and Computational Social Systems Dalian, China 24–26 July 2017 181 185
  19. [24]Candra H. Yuwono M. Chai R. Handojoseno A. Elamvazuthi I. Nguyen H.T. Su S. Investigation of window size in classification of EEG emotion signal with wavelet entropy and support vector machine Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Milan, Italy 25–29 August 2015 7250 7253 26737965
  20. [25]Zheng W.L. Zhu J.Y. Lu B.L. Identifying Stable Patterns over Time for Emotion Recognition from EEG IEEE Trans. Affect. Comput. 2016 10.1109/TAFFC.2017.2712143 · DOI
  21. [26]Mehmood R.M. Lee H.J. Emotion classification of EEG brain signal using SVM and KNN Proceedings of the IEEE International Conference on Multimedia & Expo Workshops Turin, Italy 29 June–3 July 2015 1 5
  22. [27]Li Z. Tian X. Shu L. Xu X. Hu B. Emotion Recognition from EEG Using RASM and LSTM Proceedings of the International Conference on Internet Multimedia Computing and Service Tsingtao, China 23–25 August 2017 310 318
  23. [28]Du R. Lee H.J. Frontal alpha asymmetry during the audio emotional experiment revealed by event-related spectral perturbation Proceedings of the 2015 8th International Conference on Biomedical Engineering and Informatics (BMEI) Shenyang, China 14–16 October 2015 531 536
  24. [29]Lin Y.P. Wang C.H. Jung T.P. Wu T.L. Jeng S.K. Duann J.R. Chen J.H. EEG-Based Emotion Recognition in Music Listening IEEE Trans. Bio-Med. Eng. 2010 57 1798 1806 20442037
  25. [30]Zheng W.L. Dong B.N. Lu B.L. Multimodal emotion recognition using EEG and eye tracking data Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Chicago, IL, USA 26–30 August 2014 5040 5043 25571125
  26. [31]Liu S. Meng J. Zhang D. Yang J. Emotion recognition based on EEG changes in movie viewing Proceedings of the 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER) Montpellier, France 22–24 April 2015 1036 1039
  27. [33]Duan R. Video Evoked Emotion Recognition Based on Brain Electrical Signals Master’s Thesis Shanghai Jiaotong University Shanghai, China 2014
  28. [34]Valenza G. Citi L. Lanatá A. Scilingo E.P. Barbieri R. Revealing real-time emotional responses: A personalized assessment based on heartbeat dynamics Sci. Rep. 2014 4 4998 10.1038/srep04998 24845973 PMC4028901 · DOI
  29. [35]Valenza G. Lanata A. Scilingo E.P. The Role of Nonlinear Dynamics in Affective Valence and Arousal Recognition IEEE Trans. Affect. Comput. 2012 3 237 249 10.1109/T-AFFC.2011.30 · DOI
  30. [36]Agrafioti F. Hatzinakos D. Anderson A.K. ECG Pattern Analysis for Emotion Detection IEEE Trans. Affect. Comput. 2012 3 102 115 10.1109/T-AFFC.2011.28 · DOI · Paper page
  31. [37]Guo H.W. Huang Y.S. Lin C.H. Chien J.C. Haraikawa K. Shieh J.S. Heart Rate Variability Signal Features for Emotion Recognition by Using Principal Component Analysis and Support Vectors Machine Proceedings of the 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE) Taichung, Taiwan 31 October–2 November 2016 274 277
  32. [38]Cheng Z. Shu L. Xie J. Chen C.P. A novel ECG-based real-time detection method of negative emotions in wearable applications Proceedings of the International Conference on Security, Pattern Analysis, and Cybernetics Shenzhen, China 15–17 December 2017 296 301
  33. [39]Xu Y. Hübener I. Seipp A.K. Ohly S. David K. From the lab to the real-world: An investigation on the influence of human movement on Emotion Recognition using physiological signals Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops Kona, HI, USA 13–17 March 2017 345 350
  34. [40]Rodriguez A. Rey B. Vara M.D. Wrzesien M. Alcaniz M. Banos R.M. Perez-Lopez D. A VR-Based Serious Game for Studying Emotional Regulation in Adolescents IEEE Comput. Graph. Appl. 2015 35 65 73 10.1109/MCG.2015.8 25666600 · DOI
  35. [42]Cohen R.A. Coffman J.D. Beta-adrenergic vasodilator mechanism in the finger Circ. Res. 1981 49 1196 1201 10.1161/01.RES.49.5.1196 6117377 · DOI
  36. [43]Adsett C.A. Schottsteadt W.W. Wolf S.G. Changes in coronary blood flow and other hemodynamic indicators induced by stressful interviews Psychosom. Med. 1962 24 331 336 10.1097/00006842-196200700-00002 13859490 · DOI
  37. [44]Bandler R. Shipley M.T. Columnar organization in the midbrain periaqueductal gray: Modules for emotional expression? Trends Neurosci. 1994 17 379 389 10.1016/0166-2236(94)90047-7 7817403 · DOI · Paper page
  38. [45]Bradley M.M. Lang P.J. Measuring emotion: Behavior, feeling and physiology Cognitive Neuroscience of Emotion Lane R. Nadel L. Oxford University Press New York, NY, USA 2000 242 276
  39. [47]Dimberg U. Thunberg M. Speech anxiety and rapid emotional reactions to angry and happy facial expressions Scand. J. Psychol. 2007 48 321 328 10.1111/j.1467-9450.2007.00586.x 17669222 · DOI
  40. [48]Epstein S. Boudreau L. Kling S. Magnitude of the heart rate and electrodermal response as a function of stimulus input, motor output, and their interaction Psychophysiology 1975 12 15 24 10.1111/j.1469-8986.1975.tb03053.x 1114205 · DOI
  41. [49]Blascovich J. Mendes W.B. Tomaka J. Salomon K. Seery M. The robust nature of the biopsychosocial model challenge and threat: A reply to Wrigth and Kirby Pers. Soc. Psychol. Rev. 2003 7 234 243 10.1207/S15327957PSPR0703_03 12788689 · DOI
  42. [50]Contrada R. T-wave amplitude: On the meaning of a psychophysiological index Biol. Psychol. 1992 33 249 258 10.1016/0301-0511(92)90036-T 1525299 · DOI
  43. [51]Christie I. Friedman B. Autonomic specificity of discrete emotion and dimensions of affective space: A multivariate approach Int. J. Psychophysiol. 2004 51 143 153 10.1016/j.ijpsycho.2003.08.002 14693364 · DOI · Paper page
  44. [52]Peter C. Ebert E. Beikirch H. A wearable multi-sensor system for mobile acquisition of emotion-related physiological data Proceedings of the International Conference on Affective Computing and Intelligent Interaction Beijing, China 22 October 2005 Springer Berlin/Heidelberg, Germany 2005 691 698
  45. [53]Lisetti C.L. 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 · DOI
  46. [54]Costa J. Adams A.T. Jung M.F. Guimbretière F. Choudhury T. EmotionCheck: A Wearable Device to Regulate Anxiety through False Heart Rate Feedback Getmob. Mob. Comput. Commun. 2017 21 22 25 10.1145/3131214.3131222 PMC6435301 30923745 · DOI
  47. [55]Wen W. Liu G. Cheng N. Wei J. Shangguan P. Huang W. Emotion Recognition Based on Multi-Variant Correlation of Physiological Signals IEEE Trans. Affect. Comput. 2014 5 126 140 10.1109/TAFFC.2014.2327617 · DOI
  48. [56]Monajati M. Abbasi S.H. Shabaninia F. Shamekhi S. Emotions States Recognition Based on Physiological Parameters by Employing of Fuzzy-Adaptive Resonance Theory Int. J. Intell. Sci. 2012 2 166 176 10.4236/ijis.2012.224022 · DOI
  49. [57]Wu G. Liu G. Hao M. The Analysis of Emotion Recognition from GSR Based on PSO Proceedings of the International Symposium on Intelligence Information Processing and Trusted Computing Huanggang, China 28–29 October 2010 360 363
  50. [58]Das P. Khasnobish A. Tibarewala D.N. Emotion recognition employing ECG and GSR signals as markers of ANS Proceedings of the Advances in Signal Processing Pune, India 9–11 June 2016
  51. [59]Poh M.Z. Swenson N.C. Picard R.W. A wearable sensor for unobtrusive, long-term assessment of electrodermal activity IEEE Trans. Biomed. Eng. 2010 57 1243 1252 20172811
  52. [60]Poh M.Z. Loddenkemper T. Swenson N.C. Goyal S. Madsen J.R. Picard R.W. Continuous monitoring of electrodermal activity during epileptic seizures using a wearable sensor Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Buenos Aires, Argentina 31 August–4 September 2010 4415 4418 21095760
  53. [61]Bloom L.J. Trautt G.M. Finger pulse volume as a measure of anxiety: Further evaluation Psychophysiology 1977 14 541 544 10.1111/j.1469-8986.1977.tb01195.x 928604 · DOI
  54. [62]Gray J.A. McNaughton N. The Neuropsychology of Anxiety: An Enquiry into the Functions of the Septo-hippocampal System Oxford University Press Oxford, UK 2000
  55. [63]Ekman P. Levenson R.W. Friesen W.V. Autonomic nervous system activity distinguishes among emotions Science 1983 221 1208 1210 10.1126/science.6612338 6612338 · DOI · Paper page
  56. [64]Davidson R.J. Schwartz G.E. Patterns of cerebral lateralization during cardiac biofeedback versus the self-regulation of emotion: Sex differences Psychophysiology 1976 13 62 68 10.1111/j.1469-8986.1976.tb03339.x 1244632 · DOI
  57. [65]Cacioppo J.T. Berntson G.G. Larsen J.T. Poehlmann K.M. Ito T.A. The psychophysiology of emotion The Handbook of Emotion 2nd ed. Lewis R. Haviland-Jones J.M. Guilford Press New York, NY, USA 2000 173 191
  58. [66]Eisenberg N. Fabes R.A. Bustamante D. Mathy R.M. Miller P.A. Lindholm E. Differentiation of vicariously induced emotional reactions in children Dev. Psychol. 1988 24 237 246 10.1037/0012-1649.24.2.237 · DOI
  59. [68]Drummond P.D. Facial flushing during provocation in women Psychophysiology 1999 36 325 332 10.1017/S0048577299980344 10352555 · DOI
  60. [69]James W. What is an emotion? Mind 1884 9 188 205 10.1093/mind/os-IX.34.188 · DOI · Paper page
  61. [71]Damasio A.R. Emotion in the perspective of an integrated nervous system Brain Res. Rev. 1998 26 83 86 10.1016/S0165-0173(97)00064-7 9651488 · DOI · Paper page
  62. [72]Wong W.M. Tan A.W. Loo C.K. Liew W.S. PSO optimization of synergetic neural classifier for multichannel emotion recognition Proceedings of the 2010 Second World Congress on Nature and Biologically Inspired Computing (NaBIC) Kitakyushu, Japan 15–17 December 2010 316 321
  63. [73]Mirmohamadsadeghi L. Yazdani A. Vesin J.M. Using cardio-respiratory signals to recognize emotions elicited by watching music video clips Proceedings of the 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP) Montreal, QC, Canada 21–23 September 2016 1 5
  64. [74]Wu C.K. Chung P.C. Wang C.J. Representative Segment-Based Emotion Analysis and Classification with Automatic Respiration Signal Segmentation IEEE Trans. Affect. Comput. 2012 3 482 495 10.1109/T-AFFC.2012.14 · DOI
  65. [75]Frijda N.H. Moods, emotion episodes, and emotions Handbook of Emotions Lewis M. Haviland J.M. Guilford Press New York, NY, USA 1993 381 403
  66. [78]Folkow B. Perspectives on the integrative functions of the ‘sympathoadrenomedullary system’ Auton. Neurosci. Basic Clin. 2000 83 101 115 10.1016/S1566-0702(00)00171-5 11593761 · DOI
  67. [80]Gouizi K. Reguig F.B. Maaoui C. Analysis physiological signals for emotion recognition Proceedings of the 2011 7th International Workshop on Systems, Signal Processing and their Applications (WOSSPA) Tipaza, Algeria 9–11 May 2011 147 150
  68. [81]Alzoubi O. Sidney K.D. Calvo R.A. Detecting Naturalistic Expressions of Nonbasic Affect Using Physiological Signals IEEE Trans. Affect. Comput. 2012 3 298 310 10.1109/T-AFFC.2012.4 · DOI
  69. [82]Jerritta S. Murugappan M. Wan K. Yaacob S. Emotion recognition from facial EMG signals using higher order statistics and principal component analysis J. Chin. Inst. Eng. 2014 37 385 394 10.1080/02533839.2013.799946 · DOI
  70. [84]Yang S. Yang G. Emotion Recognition of EMG Based on Improved L-M BP Neural Network and SVM J. Softw. 2011 6 1529 1536 10.4304/jsw.6.8.1529-1536 · DOI
  71. [85]Murugappan M. Electromyogram signal based human emotion classification using KNN and LDA Proceedings of the IEEE International Conference on System Engineering and Technology Shah Alam, Malaysia 27–28 June 2011 106 110
  72. [86]Cheng Y. Liu G.Y. Zhang H.L. The research of EMG signal in emotion recognition based on TS and SBS algorithm Proceedings of the 2010 3rd International Conference on Information Sciences and Interaction Sciences (ICIS) Chengdu, China 23–25 June 2010 363 366
  73. [87]Gong P. Ma H.T. Wang Y. Emotion recognition based on the multiple physiological signals Proceedings of the IEEE International Conference on Real-time Computing and Robotics (RCAR) Angkor Wat, Cambodia 6–10 June 2016 140 143
  74. [88]Bigirimana A. Siddique N. Coyle D.H. A Hybrid ICA-Wavelet Transform for Automated Artefact Removal in EEG-based Emotion Recognition Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics Budapest, Hungary 9–12 October 2016
  75. [89]Alickovic E. Babic Z. The effect of denoising on classification of ECG signals Proceedings of the XXV International Conference on Information, Communication and Automation Technologies Sarajevo, Bosnia Herzegovina 29–31 October 2015 1 6
  76. [90]Patel R. Janawadkar M.P. Sengottuvel S. Gireesan K. Radhakrishnan T.S. Suppression of Eye-Blink Associated Artifact Using Single Channel EEG Data by Combining Cross-Correlation with Empirical Mode Decomposition IEEE Sens. J. 2016 16 6947 6954 10.1109/JSEN.2016.2591580 · DOI
  77. [91]Nie D. Wang X.W. Shi L.C. Lu B.L. EEG-based emotion recognition during watching movies Proceedings of the International IEEE/EMBS Conference on Neural Engineering Cancun, Mexico 27 April–1 May 2011 667 670
  78. [92]Murugappan M. Murugappan S. Human emotion recognition through short time Electroencephalogram (EEG) signals using Fast Fourier Transform (FFT) Proceedings of the 2013 IEEE 9th International Colloquium on Signal Processing and its Applications (CSPA) Kuala Lumpur, Malaysia 8–10 March 2013 289 294
  79. [93]Chanel G. Ansari-Asl K. Pun T. Valence-arousal evaluation using physiological signals in an emotion recall paradigm Proceedings of the IEEE International Conference on Systems, Man and Cybernetics Montreal, QC, Canada 7–10 October 2007 Volume 37 2662 2667
  80. [94]Li X. Song D. Zhang P. Yu G. Hou Y. Hu B. Emotion recognition from multi-channel EEG data through Convolutional Recurrent Neural Network Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine Shenzhen, China 15–18 December 2016 352 359
  81. [95]Zied G. Lachiri Z. Maaoui C. Emotion recognition from physiological signals using fusion of wavelet based features Proceedings of the International Conference on Modelling, Identification and Control Sousse, Tunisia 18–20 December 2015
  82. [96]Verma G.K. Tiwary U.S. Multimodal fusion framework: A multiresolution approach for emotion classification and recognition from physiological signals Neuroimage 2013 102 162 172 10.1016/j.neuroimage.2013.11.007 24269801 · DOI · Paper page
  83. [97]Gouizi K. Maaoui C. Reguig F.B. Negative emotion detection using EMG signal Proceedings of the International Conference on Control, Decision and Information Technologies Metz, France 3–5 November 2014 690 695
  84. [98]Murugappan M. Ramachandran N. Sazali Y. Classification of human emotion from EEG using discrete wavelet transform J. Biomed. Sci. Eng. 2010 3 390 396 10.4236/jbise.2010.34054 · DOI
  85. [99]Yohanes R.E.J. Ser W. Huang G.B. Discrete Wavelet Transform coefficients for emotion recognition from EEG signals Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society San Diego, CA, USA 28 August–1 September 2012 2251 2254 23366371
  86. [100]Zong C. Chetouani M. Hilbert-Huang transform based physiological signals analysis for emotion recognition Proceedings of the IEEE International Symposium on Signal Processing and Information Technology Ajman, UAE 14–17 December 2010 334 339
  87. [101]Liu W. Zheng W.L. Lu B.L. Emotion Recognition Using Multimodal Deep Learning Proceedings of the International Conference on Neural Information Processing Kyoto, Japan 16–21 October 2016
  88. [102]Zhang J. Chen M. Hu S. Cao Y. Kozma R. PNN for EEG-based Emotion Recognition Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Budapest, Hungary 9–12 October 2016 2319 2323
  89. [103]Liu J. Meng H. Nandi A. Li M. Emotion detection from EEG recordings Proceedings of the 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Changsha, China 13–15 August 2016 1722 1727
  90. [104]Wang Y. Mo J. Emotion feature selection from physiological signals using tabu search Proceedings of the 2013 25th Chinese Control and Decision Conference Guiyang, China 25–27 May 2013 3148 3150
  91. [105]Liu Y.H. Wu C.T. Cheng W.T. Hsiao Y.T. Chen P.M. Teng J.T. Emotion recognition from single-trial EEG based on kernel Fisher’s emotion pattern and imbalanced quasiconformal kernel support vector machine Sensors 2014 14 13361 13388 10.3390/s140813361 25061837 PMC4179000 · DOI
  92. [107]Paithane A.N. Bormane D.S. Analysis of nonlinear and non-stationary signal to extract the features using Hilbert Huang transform Proceedings of the IEEE International Conference on Computational Intelligence and Computing Research Coimbatore, India 18–20 December 2014 1 4
  93. [108]Mansoorizadeh M. Charkari N.M. Multimodal information fusion application to human emotion recognition from face and speech Multimed. Tools Appl. 2010 49 277 297 10.1007/s11042-009-0344-2 · DOI
  94. [109]Shin D. Shin D. Shin D. Development of emotion recognition interface using complex EEG/ECG bio-signal for interactive contents Multimed. Tools Appl. 2017 76 11449 11470 10.1007/s11042-016-4203-7 · DOI
  95. [110]Sun B. Li L. Zuo T. Chen Y. Zhou G. Wu X. Combining Multimodal Features with Hierarchical Classifier Fusion for Emotion Recognition in the Wild Proceedings of the 16th International Conference on Multimodal Interaction Istanbul, Turkey 12–16 November 2015 481 486
  96. [111]Wang S. Du J. Xu R. Decision fusion for EEG-based emotion recognition Proceedings of the International Conference on Machine Learning and Cybernetics Guangzhou, China 12–15 July 2015 883 889
  97. [112]Vapnik V. Estimation of Dependences Based on Empirical Data Springer New York, NY, USA 2006
  98. [113]Cover T.M. Hart P.E. Nearest neighbor pattern classification IEEE Trans. Inf. Theory 1967 13 21 27 10.1109/TIT.1967.1053964 · DOI · Paper page
  99. [114]Franklin J. The elements of statistical learning: Data mining, inference and prediction Publ. Am. Stat. Assoc. 2005 27 83 85 10.1007/BF02985802 · DOI · Paper page
  100. [116]Garcia H.F. Alvarez M.A. Orozco A.A. Gaussian process dynamical models for multimodal affect recognition Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society Orlando, FL, USA 16–20 August 2016 850 853 28268457
  101. [117]Kim J. André E. Emotion recognition based on physiological changes in music listening IEEE Trans. Pattern Anal. Mach. Intell. 2009 30 2067 2083 10.1109/TPAMI.2008.26 18988943 · DOI
  102. [118]Kolodyazhniy V. Kreibig S.D. Gross J.J. Roth W.T. Wilhelm F.H. 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/j.1469-8986.2010.01170.x 21261632 · DOI
  103. [120]Salari S. Ansarian A. Atrianfar H. Robust emotion classification using neural network models Proceedings of the 2018 6th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS) Kerman, Iran 28 February–2 March 2018 190 194
  104. [121]Song T. Zheng W. Song P. Cui Z. EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks IEEE Trans. Affect. Comput. 2018 3 1 10.1109/TAFFC.2018.2817622 · DOI
  105. [123]Huang J. Xu X. Zhang T. Emotion classification using deep neural networks and emotional patches Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine Kansas City, MO, USA 13–16 November 2017 958 962
  106. [124]Kawde P. Verma G.K. Deep belief network based affect recognition from physiological signals Proceedings of the 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON) Mathura, India 26–28 October 2017 587 592
  107. [125]Alhagry S. Aly A. Reda A. Emotion Recognition based on EEG using LSTM Recurrent Neural Network Int. J. Adv. Comput. Sci. Appl. 2017 8 355 358 10.14569/IJACSA.2017.081046 · DOI
  108. [126]Liu J. Su Y. Liu Y. Multi-modal Emotion Recognition with Temporal-Band Attention Based on LSTM-RNN Advances in Multimedia Information Processing—PCM 2017 Zeng B. Huang Q. El Saddik A. Li H. Jiang S. Fan X. Lecture Notes in Computer Science Springer Cham, Switzerland 2018 Volume 10735
  109. [127]Ali M. Mosa A.H. Al Machot F. Kyamakya K. A novel EEG-based emotion recognition approach for e-healthcare applications Proceedings of the Eighth International Conference on Ubiquitous and Future Networks Vienna, Austria 5–8 July 2016 946 950
  110. [128]Thammasan N. Moriyama K. Fukui K.I. Numao M. Thammasan N. Moriyama K. Fukui K.I. Numao M. Familiarity effects in EEG-based emotion recognition Brain Inform. 2016 4 39 50 10.1007/s40708-016-0051-5 27747819 PMC5319949 · DOI
  111. [129]Xu H. Plataniotis K.N. Affective states classification using EEG and semi-supervised deep learning approaches Proceedings of the International Workshop on Multimedia Signal Processing London, UK 16–18 October 2017 1 6
  112. [130]Soleymani M. Lichtenauer J. Pun T. Pantic M. A Multimodal Database for Affect Recognition and Implicit Tagging IEEE Trans. Affect. Comput. 2012 3 42 55 10.1109/T-AFFC.2011.25 · DOI · Paper page
  113. [131]Koelstra S. Patras I. Fusion of facial expressions and EEG for implicit affective tagging Image Vis. Comput. 2013 31 164 174 10.1016/j.imavis.2012.10.002 · DOI
  114. [132]Ferdinando H. Seppänen T. Alasaarela E. Enhancing emotion recognition from ECG Signals using supervised dimensionality reduction Proceedings of the ICPRAM Porto, Portugal 24–26 February 2017 112 118
  115. [133]Wiem M.B.H. Lachiri Z. Emotion recognition system based on physiological signals with Raspberry Pi III implementation Proceedings of the International Conference on Frontiers of Signal Processing Paris, France 6–8 September 2017 20 24
  116. [134]Xu H. Plataniotis K.N. Subject independent affective states classification using EEG signals Proceedings of the IEEE Global Conference on Signal and Information Processing Orlando, FL, USA 14–16 December 2015 1312 1316
  117. [136]Li H. Qing C. Xu X. Zhang T. A novel DE-PCCM feature for EEG-based emotion recognition Proceedings of the International Conference on Security, Pattern Analysis, and Cybernetics Shenzhen, China 15–18 December 2017 389 393
  118. [137]Song T. Zheng W. Song P. Cui Z. EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks IEEE Trans. Affect. Comput. 2008 10.1109/TAFFC.2018.2817622 · DOI
  119. [138]Yang B. Han X. Tang J. Three class emotions recognition based on deep learning using staked autoencoder Proceedings of the International Congress on Image and Signal Processing, Biomedical Engineering and Informatics Beijing, China 13 October 2018
  120. [139]Zheng W. Multichannel EEG-Based Emotion Recognition via Group Sparse Canonical Correlation Analysis IEEE Trans. Cogn. Dev. Syst. 2017 9 281 290 10.1109/TCDS.2016.2587290 · DOI
  121. [140]Zhang L. Walter S. Ma X. Werner P. Al-Hamadi A. Traue H.C. Gruss S. “BioVid Emo DB”: A multimodal database for emotion analyses validated by subjective ratings Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence (SSCI) Athens, Greece 6–9 December 2016
  122. [141]Samara A. Menezes M.L.R. Galway L. Feature extraction for emotion recognition and modelling using neurophysiological data Proceedings of the International Conference on Ubiquitous Computing and Communications and 2016 International Symposium on Cyberspace and Security Xi’an, China 23–25 October 2017 138 144
  123. [142]Basu S. Jana N. Bag A. Mahadevappa M. Mukherjee J. Kumar S. Guha R. Emotion recognition based on physiological signals using valence-arousal model Proceedings of the Third International Conference on Image Information Processing Waknaghat, India 21–24 December 2015 50 55
  124. [143]Ray R.D. Emotion elicitation using films Handbook of Emotion Elicitation and Assessment Oxford University Press Oxford, UK 2007 9
  125. [144]Mühl C. Brouwer A.M. Van Wouwe N. van den Broek E. Nijboer F. Heylen D.K. Modality-specific affective responses and their implications for affective BCI Proceedings of the Fifth International Brain-Computer Interface Conference Asilomar, CA, USA 22–24 September 2011
  126. [145]Ardizzi M. Sestito M. Martini F. Umiltà M.A. Ravera R. Gallese V. When age matters: Differences in facial mimicry and autonomic responses to peers’ emotions in teenagers and adults PLoS ONE 2014 9 e110763 10.1371/journal.pone.0110763 25337916 PMC4206508 · DOI
  127. [147]Ardizzi M. Martini F. Umiltà M.A. Sestitoet M. Ravera R. Gallese V. When Early Experiences Build a Wall to Others’ Emotions: An Electrophysiological and Autonomic Study PLoS ONE 2013 8 e61004 10.1371/journal.pone.0061004 23593374 PMC3622660 · DOI
  128. [148]Ardizzi M. Umiltà M.A. Evangelista V. Liscia A.D. Ravera R. Gallese V. Less Empathic and More Reactive: The Different Impact of Childhood Maltreatment on Facial Mimicry and Vagal Regulation PLoS ONE 2016 11 e0163853 10.1371/journal.pone.0163853 27685802 PMC5042550 · DOI
  129. [149]Matzke B. Herpertz S.C. Berger C. Fleischer M. Domes G. Facial Reactions during Emotion Recognition in Borderline Personality Disorder: A Facial Electromyography Study Psychopathology 2014 47 101 110 10.1159/000351122 24021701 · DOI
  130. [150]Zantinge G. Van R.S. Stockmann L. Swaab H. Concordance between physiological arousal and emotion expression during fear in young children with autism spectrum disorders Autism Int. J. Res. Pract. 2018 10.1177/1362361318766439 29595334 PMC6463270 · DOI

Cited by

Sorted by citations

Authors
Shu, L., Xie, J., Yang, M., Li, Z., Li, Z., Liao, D., Xu, X., & Yang, X.
Affiliations
  • South China University of Technology (CN)
  • School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China
Corresponding author
Xu, Xiangmin
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2018-06-28
Volume
18
Issue
7
Pages
2074
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s18072074
PMC
PMC6069143
OpenAlex
W2810418809
Cited by
923
References
15
Citations by year
2026: 87 · 2025: 127 · 2024: 136 · 2023: 146 · 2022: 164 · 2021: 130 · 2020: 94 · 2019: 36 · 2018: 3
FWCI
10.48
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Fundamental Research Funds fo (2017MS041)
  • Science and Technology Program of Guangzhou (201704020043)
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
  • Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
Keywords
emotion recognition, physiological signals, emotion models, emotion induction, physiological traits, emotion classification, health monitoring
MeSH terms
Emotions, Humans, Models, Psychological, Recognition, Psychology, Emotional Intelligence

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