. Tableau-a, 5 -Composition des blocs sélectionnés parmi ceux du corpus eNTERFACE, p.6

. Le and . .. Le-sens-commun, , p.13

]. .. , Le processus émotionnel dans la prise de décision : l'hypothèse des marqueurs somatiques formulée par Damasio, p.17, 2008.

, La métaphore de l'Iceberg de Freud appliquée au processus émotionnel.La force de flottabilité est liée à la masse de liquide déplacée par l'objet, p.19

. .. Schéma-du-cerveau,

, Exemple d'enregistrements de signaux périphériques EOG, EMG et ECG sur une durée de 10 secondes

, Les trois modes de représentation des changements dans les composantes de l'émotion : l'inconscience, la conscience et la verbalisation, tiré de Scherer, 2005.

, Le cône des émotions de Plutchik [Plutchik, 1984.

, Les 8 émotions élémentaires sont placées par opposition au centre de la roue

R. Circumplex-de and B. , , 1998.

, Le disque interne représente un schéma de l'affect central (core affect), et le disque extérieur représente plusieurs émotions prototypiques liées à leurs caractéristiques affectives, p.43

, Exemple d'une représentation des axes de valence et d'activation à l'aide du Self-Assessment Manikin

. Le and . .. De-feeltrace, , vol.46

.. .. Le-système-d'annotation-de-gtrace,

, VL : très faible, L : faible, M : Moyen, H : élevé, VH : très élevé, p.48, 2004.

. Savran, , p.52, 2006.

, Images issues de la base de données IAPS (de gauche à droite : classe "excité positif", classe "excité négatif", classe "calme")

, Répartition des stimuli de la base ENTERFACE'06 dans le plan Valence/Activation [Xu and (Kostas) Plataniotis, 2012.

H. Lalv, . Lahv, and . Hahv)-[koelstra, Répartition des stimuli de la base DEAP dans le plan Valence/Activation pour les quatre conditions, 2012.

, La couleur traduit l'appréciation (le rouge foncé est une faible appréciation), et la taille traduit la dominance, de petite à élevée

, Répartition des stimuli visuels dans l'espace Valence/Activation, p.62

, Description du degré d'imminence et de la gravité dans l'annotation des segments du corpus SAFE

, Répartition de l'ensemble des regroupements sur les dimensions "Imminence" et "Intensité"

, Emplacements des 20 électrodes EEG du casque B-Alert X-24, p.66

, Un artefact oculaire est présent dans cette portion de signaux (encadré rouge), de forte amplitude sur les électrodes frontales, il s'atténue à l'arrière du casque

. Extraits-de-signaux-eog and . .. Ecg,

. .. , Position des électrodes EOG et EMG. Quatre électrodes sont utilisées pour enregistrer l'EOG et deux pour l'EMG (Zygomatique majeur), p.68

, Participant lors d'une session de l'expérience

. .. , Plan de la configuration de la salle lors de l'expérience, p.69

, Description d'un essai correspondant à la diffusion d'un stimulus, p.71

, Description d'un essai dans le protocole ENTERFACE'06. la diffusion des stimuli est suivie d'une phase d'auto-évaluation statique

, Description du protocole pour une vidéo en plein écran. La diffusion du stimuli est suivie d'une phase d'auto-évaluation dynamique, p.72

, Exemple d'annotation des 9 images de calibrage (1-9) sur la dimension de valence

, Interface d'annotation globale

, Interface d'annotation dynamique

, Les données de la phase d'apprentissage (i.e. un bloc) sont en vert et les données de la phase de test sont en rouge

, Les données de la phase d'apprentissage (i.e. un bloc) sont en vert et les données de la phase de test sont en rouge

. .. , Taux de bonnes reconnaissance obtenus pour chaque sujet en fonction de la configuration adoptée pour le système de classification, p.101

. .. Eeg, Bandes fréquentielles des principaux rythmes

, Les principaux signaux périphériques utilisés dans le cadre de la reconnaissance de l'émotion en informatique affective

, Liste et description de publications sur le reconnaissance de l'émotion utilisant les signaux physiologiques périphériques

, Liste et description de publications sur le reconnaissance de l'émotion utilisant les signaux périphériques et l'EEG

.. .. Différentes-combinaisons-de-modalités,

, Liste et description de publications sur la reconnaissance de l'émotion utilisant des systèmes multi-modaux

, KNN : K-Nearest Neighbor), Principaux résultats dans la littérature sur la reconnaissance des émotions basée sur l'EEG (PSD : Power Spectral Density

, Les principales théories des émotions basiques

, Les bases de données validées de stimuli à contenu émotionnel (Val. : Valence, Act. : Activation, Dom. : Dominance, App.-Av. : Approach-Avoidance), p.49

. .. , Comparatif des bases de données existantes et accessibles, p.51

. Savran, Références des blocs sélectionnés dans le protocole ENTERFACE'06, p.61, 2006.

. Moyenne, Écart-type) des images IAPS sélectionnées pour le protocole de calibrage

, Degré d'accord en fonction des valeurs de Kappa

, Coefficient de la corrélation de Pearson entre les scores IAPS et l'auto-évaluation par session de chaque sujet du corpus EMOGEE sur les images de calibrage, p.79

, Coefficients de la corrélation de Pearson entre l'auto-évaluation de chaque session par sujet du corpus EMOGEE sur les images de calibrage, p.80

, Coefficients de corrélation de Pearson entre les scores IAPS et les auto-évaluations par sujet de la base eNTERFACE'06 [Xu and (Kostas) Plataniotis, p.80, 2012.

. .. , Coefficient de la corrélation de Pearson entre les scores IAPS et l'auto-évaluation des blocs eNTERFACE'06 par sujet du corpus EMOGEE, p.81

, Calcul de ? pour chaque paire d'annotateurs pour les évaluations sur l'axe de valence et l'axe d'activation des stimuli visuels

, Coefficients ? F en fonction du nombre d'annotateurs pour l'annotation selon les dimensions de valence et d'activation des stimuli visuels, p.82

, Moyenne des coefficients ? pour l'annotation globale et dynamique des stimuli audio-visuels suivant les dimensions de valence et d'activation et leur regroupement

, Calcul de ? pour chaque paire d'annotateurs pour les évaluations sur l'axe de valence et l'axe d'activation des stimuli audio-visuels

, Coefficients ? F en fonction du nombre d'annotateurs pour l'annotation globale selon les dimensions de valence et d'activation des stimuli audio-visuels, vol.83

, Coefficients ? F en fonction du nombre d'annotateurs pour l'annotation dynamique selon les dimensions de valence et d'activation des stimuli audio-visuels, vol.84

, Moyenne des coefficients ? en fonction du type de stimuli annotés, p.84

, Ces descripteurs sont extraits pour chaque signal d'électrode EEG, Liste des descripteurs proposés dans nos travaux

, Caractéristiques extraites pour la reconnaissance des émotions basée sur l

, Résultats obtenus pour chaque sujet en fonction de l'utilisation de différentes caractéristiques. Les meilleurs scores de chaque sujet sont indiqués en gras, p.97

, Résultats obtenus avec les CSP pour 20 composantes. Les scores les plus importants sont surlignés

, Résultats obtenus avec les combinaisons de CSP pour 20 composantes.Les scores les plus importants sont surlignés

, Résultats obtenus avec les combinaisons de CSP pour 40 composantes. Les scores les plus importants sont surlignés

. .. , Caractéristiques extraites pour la reconnaissance des émotions basée sur l'EEG. Les caractéristiques que nous proposons sont en gras, p.99

, Taux de bonne reconnaissance (selon la dimension de valence) pour un sousensemble (le meilleur) des caractéristiques proposées : Moments (Mts), SF, SCF, et CSP pour une dimension de D CSP = 20 pour chacun des 5 sujets (S1 à S2). Les meilleures précisions sont en caractère gras

, Meilleurs scores avec leurs caractéristiques associées pour chaque sujet pour une classification binaire suivant la dimension de Valence, p.102

, Meilleurs scores avec leurs caractéristiques associées pour chaque sujet pour une classification binaire suivant la dimension d'Activation, p.102

A. Conneau and S. Essid, Assessment of new spectral features for eeg-based emotion recognition, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014.
URL : https://hal.archives-ouvertes.fr/hal-02287334

A. Conneau, A. Hajlaoui, M. Chetouani, and S. Essid, EMOGEE : A new multimodal dataset for dynamic EEG-based emotion recognition with audio-visual stimuli

A. Bibliographie-ralph, Recognizing Emotion From Facial Expressions : Psychological and Neurological Mechanisms, Behavioral and Cognitive Neuroscience Reviews, vol.1, issue.1, p.25, 2002.

F. Agrafioti, D. Hatzinakos, and A. K. Anderson, ECG pattern analysis for emotion detection, IEEE Transactions on Affective Computing, vol.3, issue.1, p.67, 2012.

O. Alaoui-ismaïli, . Robin, . Rada, E. Dittmar, and . Vernet-maury, Basic emotions evoked by odorants : Comparison between autonomic responses and self-evaluation, Physiology and Behavior, vol.62, issue.4, p.49, 1997.

O. Alzoubi, R. A. Calvo, and R. H. Stevens, Classification of EEG for Affect Recognition : An Adaptive Approach, AI 2009 : Advances in Artificial Intelligence, vol.35, p.49, 2009.

J. Anttonen and V. Surakka, Emotions and heart rate while sitting on a chair, Proceedings CHI 2005, p.24, 2005.

M. B. Arnold, Emotion and personality, p.18, 1960.

A. B. Xiii and . Bibliographie,

A. F. Ax, The physiological differentiation between fear and anger in humans. Psychosomatic Medicine, vol.15, p.23, 1953.

J. N. Bailenson, E. D. Pontikakis, I. B. Mauss, J. J. Gross, M. E. Jabon et al., Real-time classification of evoked emotions using facial feature tracking and physiological responses, International Journal of Human Computer Studies, vol.66, issue.5, p.31, 2008.

E. Y. Bann and J. J. Bryson, The Conceptualisation of Emotion Qualia : Semantic Clustering of Emotional Tweets, Proceedings of the 13th Neural Computation and Psychology Workshop (NCPW13), number July, vol.41, p.42, 2012.

P. Bard, A diencephalic mechanism for the expression of rage with special reference to the sympathetic nervous system, American Journal of Physiology, p.14, 1928.

L. Barrett, Discrete Emotions or Dimensions ? The Role of Valence Focus and Arousal Focus, Cognition & Emotion, vol.12, issue.4, p.45, 1998.

A. Teodiano-freire-bastos-filho, A. C. Ferreira, S. Atencio, D. Arjunan, and . Kumar, Evaluation of feature extraction techniques in emotional state recognition, IEEE Proceedings of 4th International Conference on Intelligent Human Computer Interaction (IHCI), p.35, 2012.

A. Basu and A. Halder, Facial expression and EEG signal based classification of emotion, International Conference on Electronics, Communication and Instrumentation (ICECI), p.29, 2014.

T. Baumgartner, M. Esslen, and L. Jäncke, From emotion perception to emotion experience : Emotions evoked by pictures and classical music, International Journal of Psychophysiology, vol.60, issue.1, p.49, 2006.

T. Baumgartner, K. Lutz, C. F. Schmidt, and L. Jäncke, The emotional power of music : how music enhances the feeling of affective pictures, Brain Research, vol.1075, issue.1, p.49, 2006.

C. Kent, M. L. Berridge, and . Kringelbach, Neuroscience of affect : brain mechanisms of pleasure and displeasure, Current opinion in neurobiology, vol.23, issue.3, pp.294-303, 2013.

C. Kent, P. Berridge, and . Winkielman, What is an unconscious emotion ?(The case for unconscious" liking"), Cognition & Emotion, vol.17, issue.2, p.48, 2003.

B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe, and K. Müller, Optimizing spatial filters for robust EEG single-trial analysis, IEEE Signal Processing Magazine, vol.20, p.90, 2008.

K. Blinowska, P. Durka, and ;. Bibliographie, Electroencephalography (eeg), Wiley Encyclopedia of Biomedical Engineering, issue.5, 2006.

S. Bloch, M. Lemeignan, and T. N. Aguilera, Specific respiratory patterns distinguish among human basic emotions, International Journal of Psychophysiology, vol.11, issue.2, p.24, 1991.

N. H. Frans-a-boiten, C. J. Fridja, and . Wientjes, Emotions and respiratory patterns : Review and critical analysis, International Journal of Psychophysiology, vol.17, issue.94, p.24, 1994.

D. Bos, EEG-based Emotion Recognition. The Influence of Visual and Auditory Stimuli, p.49, 2006.

M. M. Bradley and P. J. Lang, Measuring emotion : The self-assessment manikin and the semantic differential, Journal of Behavior Therapy and Experimental Psychiatry, vol.25, issue.1, p.94, 1944.

M. M. Bradley and P. J. Lang, Affective Norms for English Words ( ANEW ) : Instruction Manual and Affective Ratings. Psychology, Technical(C-1), vol.38, p.49, 1999.

M. M. Bradley and P. J. Lang, IADS-2) : Affective Ratings of Sounds and Instruction Manual. Technical Report 2, p.49, 2007.

. Margaret and . Brbradley, Emotion and motivation, Handbook of psychophysiology, chapter 22, p.24, 2000.

E. Joseph and D. Bronzino, Principles of Electroencephalography. The Biomedical Engineering Handbook : Second Edition, 2000.

T. Brosch and D. Sander, Comment : The Appraising Brain : Towards a Neuro-Cognitive Model of Appraisal Processes in Emotion, Emotion Review, vol.5, issue.2, pp.163-168, 2013.

J. Cai, G. Liu, and M. Hao, The Research on Emotion Recognition from ECG Signal, International Conference on Information Technology and Computer Science, p.24, 2009.

. Walter-b-cannon, The James-Lange theory of emotions : a critical examination and an alternative theory. The American journal of psychology, vol.39, p.14, 1927.

G. Caridakis and G. Castellano, Multimodal emotion recognition from expressive faces, body gestures and speech, Artificial intelligence, vol.247, p.31, 2007.

S. Carvalho, J. Leite, S. Galdo-Álvarez, and O. F. Gonçalves, The Emotional Movie Database (EMDB) : a self-report and psychophysiological study, Applied psychophysiology and biofeedback, vol.37, issue.4, p.49, 2012.

G. Castellano, D. Santiago, A. Villalba, and . Camurri, Recognising Human Emotions from Body Movement and Gesture Dynamics, Affective Computing and Intelligent Interaction, vol.4738, p.25, 2007.

G. Castellano, L. Kessous, and G. Caridakis, Emotion recognition through multiple modalities : Face, body gesture, speech. Lecture Notes in Computer Science (including subseries, Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, vol.29, p.30, 2008.

G. Chanel, Emotion assessment for affective computing based on brain and peripheral signals, p.22, 2009.

G. Chanel, J. Kronegg, D. Grandjean, and T. Pun, Emotion assessment : Arousal evaluation using EEGs and peripheral physiological signals. Multimedia Content Representation, Classification and Security, p.34, 2006.

G. Chanel, T. Ansari-asl, and . Pun, Valence-arousal evaluation using physiological signals in an emotion recall paradigm, IEEE International Conference on Systems, Man and Cybernetics, vol.41, p.49, 2007.

Y. Chuan, . Chang, C. J. Jeng-shiun-tsai, P. Wang, and . Chung, Emotion recognition with consideration of facial expression and physiological signals, CIBCB 2009 -Proceedings, p.31, 2009.

D. Chen and P. Dalton, The effect of emotion and personality on olfactory perception. Chemical senses, vol.30, p.49, 2005.

L. S. Chen, H. Tao, T. S. Huang, T. Miyasato, and R. Nakatsu, Emotion recognition from audiovisual information, IEEE Second Workshop on Multimedia Signal Processing, p.31, 1998.

B. Cheng and G. Liu, Emotion recognition from surface EMG signal using wavelet transform and neural network, Proceedings of The 2nd International Conference on Bioinformatics and Biomedical Engineering (ICBBE), vol.24, p.67, 2008.

C. Clavel, Fear-type emotions of the SAFE Corpus : annotation issues, Proc. of LREC, vol.27, p.29, 2006.

C. Clavel, I. Vasilescu, L. Devillers, G. Richard, T. Ehrette et al., The SAFE Corpus : illustrating extreme emotions in dynamic situations, First International Workshop on Emotion : Corpora for Research on Emotion and Affect (International conference on Language Resources and Evaluation, p.60, 2006.

C. Clavel, L. Devillers, G. Richard, I. Vasilescu, and T. Ehrette, Detection and analysis of abnormal situations through fear-type acoustic manifestations, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing -Proceedings, vol.4, pp.21-24, 2007.

A. B. Xvi and . Bibliographie,

C. Clavel, I. Vasilescu, and L. Devillers, Fiction support for realistic portrayals of fear-type emotional manifestations, Computer Speech and Language, vol.25, issue.1, p.63, 2011.

J. A. Coan and J. Allen, Handbook of emotion elicitation and assessment, p.49, 2007.

J. Cohen, A Coefficient of Agreement for Nominal Scales, Educational and Psychological Measurement, vol.20, issue.1, p.77, 1960.

J. Cohen, Weighted kappa : Nominal scale agreement provision for scaled disagreement or partial credit, Psychological bulletin, vol.70, issue.4, p.78, 1968.

I. Constant and N. Sabourdin, The EEG signal : A window on the cortical brain activity, vol.22, pp.539-552, 2012.

G. Coppin and D. Sander, Contemporary Theories and Concepts in the Psychology of Emotions. Emotion-oriented Systems, vol.18, p.40, 2012.

J. L. Cotton, A review of research on Schachter's theory of emotion and the misattribution of arousal, European Journal of Social Psychology, vol.11, issue.4, p.16, 1981.

R. Cowie, E. Douglas-cowie, S. Savvidou, E. Mcmahon, M. Sawey et al., FEELTRACE' : An instrument for recording perceived emotion in real time, In ISCA Workshop on Speech & Emotion, p.45, 2000.

I. Daly, A. Malik, F. Hwang, E. Roesch, J. Weaver et al., Neural correlates of emotional responses to music : An EEG study, Neuroscience Letters, vol.573, p.49, 2014.

A. Damasio, Descartes' error : Emotion, reason and the human brain. Random House, vol.17, 2008.

A. R. Damasio, The Somatic Marker Hypothesis and the Possible Functions of the Prefrontal Cortex, Philosophical Transactions of the Royal Society B : Biological Sciences, vol.351, p.48, 1996.

E. S. Dan-glauser and K. R. Scherer, The Geneva affective picture database (GAPED) : a new 730-picture database focusing on valence and normative significance, Behavior Research Methods, vol.43, issue.2, p.49, 2011.

C. Darwin, The expression of the emotions in man and animals, The American Journal of the Medical Sciences, vol.19, p.67

R. J. Davidson, Affect, cognition, and hemispheric specialization, Emotions, cognition, and behavior, vol.1, p.20, 1984.

H. Davis, T. Mast, Y. Nobuo, and S. Zerlin, The slow response of the human cortex to auditory stimuli : recovery process, Electroencephalography and clinical neurophysiology, vol.21, issue.2, p.21, 1966.

A. Dhall, R. Goecke, J. Joshi, M. Wagner, and T. Gedeon, Emotion recognition in the wild challenge, International Conference On Multimodal Interaction, p.27, 2013.

K. Sidney and J. Kory, A review and meta-analysis of multimodal affect detection systems, ACM Computing Surveys (CSUR), vol.47, issue.3, p.29, 2015.

S. Dobri?ek, R. Gaj?ek, F. Miheli?, N. Pave?i?, and V. ?truc, Towards efficient multi-modal emotion recognition, International Journal of Advanced Robotic Systems, vol.10, p.31, 2013.

R. Du and H. Lee, Power spectral performance analysis of EEG during emotional auditory experiment, ICALIP 2014 -2014 International Conference on Audio, Language and Image Processing, p.49, 2015.

S. Dubnov, Generalization of spectral flatness measure for non-gaussian linear processes, Signal Processing Letters, issue.1, p.92, 2004.

G. Benjamin-duchenne, Mécanisme de la physionomie humaine, ou analyse électro-physiologique de l'expression des passions

P. Ekman, Expression and the nature of emotion, p.50, 1963.

P. Ekman, An argument for basic emotions, vol.6, p.38, 1920.

P. Ekman, Basic emotions, Handbook of cognition and emotion, vol.38, p.41, 1999.

P. Ekman, Darwin and facial expression : A century of research in review. Ishk, p.25, 2006.

P. Ekman and W. V. Friesen, Measuring facial movement, Environmental Psychology and Nonverbal Behavior, vol.1, issue.1, p.20, 1976.

P. Ekman, R. W. Levenson, and W. V. Friesen, Autonomic nervous system activity distinguishes among emotions, Science, vol.221, issue.4616, pp.1208-1210, 1983.

M. S. Moataz-el-ayadi, F. Kamel, and . Karray, Survey on speech emotion recognition : Features, classification schemes, and databases, Pattern Recognition, vol.44, issue.3, p.27, 2011.

P. C. Ellsworth and K. R. Scherer, Appraisal processes in emotion, Handbook of affective sciences, chapter 29, p.47, 2003.

A. B. Xviii and . Bibliographie,

S. Essid, Classification automatique des signaux audio-frequences : reconnaissance des instruments de musique, p.91, 2005.
URL : https://hal.archives-ouvertes.fr/pastel-00002738

B. Fasel and J. Luettin, Automatic facial expression analysis : a survey, Pattern Recognition, vol.36, issue.1, p.27, 2003.

L. Barrett and J. Russell, Independence and Bipolarity in the Structure of Current Affect, Journal of Personality and Social Psychology, vol.74, issue.4, pp.967-984, 1998.

A. Feleky, The influence of the emotions on respiration, Journal of Experimental Psychology, p.24, 1916.

R. Gereon, R. S. Fink, U. Frackowiak, R. E. Pietrzyk, and . Passingham, Multiple nonprimary motor areas in the human cortex, Journal of neurophysiology, vol.77, issue.4, p.21, 1997.

R. J. Johnny, K. R. Fontaine, E. B. Scherer, P. C. Roesch, and . Ellsworth, The World of Emotions Is Not Two-Dimensional, Psychological Science, vol.18, issue.12, p.47, 2007.

I. Fried, C. Mateer, G. Ojemann, R. Wohns, and P. Fedio, Organization of visuospatial functions in human cortex. Evidence from electrical stimulation, Brain : a journal of neurology, issue.2, p.21, 1982.

N. H. Frijda, The emotions : Studies in emotion & social interaction. Paris : Maison de Sciences de l'Homme, p.18, 1986.

N. H. Frijda, The Laws of Emotion, American Psychologist, vol.43, issue.5, p.18, 1988.

D. Galati and B. Sini, Les structures sémantiques du lexique français des émotions, Les émotions dans les interactions, p.42, 2000.

B. M. Antje, M. J. Gerdes, G. W. Wieser, and . Alpers, Emotional pictures and sounds : a review of multimodal interactions of emotion cues in multiple domains, Frontiers in psychology, vol.5, p.49, 2014.

A. Gerrards-hesse, K. Spies, and F. W. Hesse, Experimental inductions of emtoional states and their effectiveness : A review, British Journal of Psychology, vol.85, p.49, 1994.

O. Gillet and G. Richard, Automatic transcription of drum loops, IEEE International Conference on Acoustics, Speech, and Signal Processing, vol.4, p.91, 2004.

D. Glowinski, A. Camurri, G. Volpe, N. Dael, and K. Scherer, Technique for automatic emotion recognition by body gesture analysis, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, p.25, 2008.

V. Goossens, Les noms de sentiment, Lidil, vol.32, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00644446

J. J. Gross and R. W. Levenson, Emotion Elicitation Using Films, vol.9, p.49, 1995.

H. Gunes and M. Pantic, Automatic measurement of affect in dimensional and continuous spaces : why, what, and how ?*, Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research (MB '10), p.47, 2010.

H. Gunes and M. Piccardi, Bimodal face and body gesture database for automatic analysis of human nonverbal affective behavior, Proceedings -International Conference on Pattern Recognition, vol.1, p.30, 2006.

H. Gunes and M. Piccardi, Bi-modal emotion recognition from expressive face and body gestures, Journal of Network and Computer Applications, vol.30, issue.4, p.29, 2007.

H. Gunes, M. Piccardi, and M. Pantic, From the Lab to the Real World : Affect Recognition Using Multiple Cues and Modalities, Affective Computing : Focus on Emotion Expression, Synthesis, and Recognition, p.13, 2008.

S. Gupta, A. Mehra, and N. Gandhi, Recognizing Emotions using Speech and Blood Volume Pulse, Proc. Int. Conf. on Computational Intelligence and Information Technology, p.24, 2012.

A. Haag, S. Goronzy, P. Schaich, and J. Williams, Emotion recognition using bio-sensors : First steps towards an automatic system. Affective dialogue systems, p.26, 2004.

A. Haag, S. Goronzy, P. Schaich, and J. Williams, Emotion Recognition Using Bio-sensors : First Steps towards an Automatic System. Affective dialogue systems, p.67, 2004.

M. Haak, S. Bos, S. Panic, and L. J. Rothkrantz, Detecting Stress Using Eye Blinks And Brain Activity From EEG Signals, Proceeding of the 1st driver car interaction and interface (DCII 2008), volume 10th Inter, vol.24, p.30, 2009.

D. Hagemann, S. R. Waldstein, and J. F. Thayer, Central and autonomic nervous system integration in emotion, Brain and Cognition, vol.52, issue.1, pp.79-87, 2003.

S. Haq, J. B. Philip, and . Jackson, Multimodal emotion recognition, Machine audition : principles, algorithms and systems, p.29, 2010.

J. William, M. B. Havlena, and . Holbrook, The varieties of consumption experience : comparing two typologies of emotion in consumer behavior, Journal of Consumer Research, vol.13, issue.3, p.44, 1986.

A. Heraz, R. Razaki, and C. Frasson, Using machine learning to predict learner emotional state from brainwaves, Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007), p.28, 2007.

A. B. Xx and . Bibliographie,

U. Hess and P. Thibault, Darwin and emotion expression. The American psychologist, vol.64, p.14, 2009.

B. Hjorth, EEG analysis based on time domain properties, Electroencephalography and Clinical Neurophysiology, vol.29, issue.3, p.34, 1970.

R. Horlings, D. Datcu, and L. J. Rothkrantz, Emotion recognition using brain activity, Proceedings of the 9th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing, vol.34, p.49, 2008.

E. Hudlicka and H. Gunes, Benefits and Limitations of Continuous Representations of Emotions in Affective Computing : Introduction to the Special Issue, International Journal of Synthetic Emotions, vol.3, issue.1, p.47, 2012.

J. Mcv, W. Hunt, E. E. Marie-louise-cole, and . Reis, Situational Cues Distinguishing Anger, Fear, and Sorrow, The American Journal of Psychology, vol.71, issue.1, p.16, 1958.

C. E. Izard, Basic emotions, relations among emotions, and emotion-cognition relations, Psychological Review, vol.99, issue.3, p.41, 1992.

A. Jaimes and N. Sebe, Multimodal human-computer interaction : A survey. Computer Vision and Image Understanding, vol.108, p.29, 2007.

W. James, What is an emotion ? Mind, vol.9, p.23, 1884.

E. Jang, B. Park, S. Kim, M. Chung, M. Park et al., Classification of Human Emotions from Physiological signals using Machine Learning Algorithms, The Sixth International Conference on Advances in Computer-Human Interactions (ACHI 2013), vol.25, p.67, 2013.

N. Jatupaiboon, P. Setha-pan-ngum, and . Israsena, Emotion classification using minimal EEG channels and frequency bands, The 2013 10th International Joint Conference on Computer Science and Software Engineering (JCSSE), p.49, 2013.

N. Jatupaiboon, Real-time EEG-based happiness detection system, Setha Pan-ngum, and Pasin Israsena, p.618649, 2013.

R. Jenke, A. Peer, and M. Buss, Effect-Size-Based Electrode and Feature Selection for Emotion Recognition from EEG, vol.34, p.97, 2013.

X. Jie, R. Cao, and L. Li, Emotion recognition based on the sample entropy of EEG, Bio-medical materials and engineering, vol.24, issue.1, p.35, 2014.

C. Joder, S. Essid, and G. Richard, Temporal integration for audio classification with application to musical instrument classification, IEEE Transactions on Audio, Speech and Language Processing, vol.17, issue.1, p.36, 2009.
URL : https://hal.archives-ouvertes.fr/hal-02652782

P. N. Johnson-laird and K. Oatley, The language of emotions : An analysis of a semantic field, Cognition & Emotion, vol.3, issue.2, p.42, 1989.

J. D. Johnston, Transform coding of audio signals using perceptual noise criteria, IEEE Journal on Selected Areas in Communications, vol.6, issue.2, p.92, 1988.

K. Kallinen, Emotion related psychophysiological responses to listening to music with eyes-open versus eyes-closed : Electrodermal (EDA), Proceedings of the 8th International Conference on Music Perception & Cognition (ICMPC8), p.24, 2004.

S. Kastner and L. G. Ungerleider, Mechanisms of Visual Attention in the Human Cortex, Annual Review of Neuroscience, vol.23, p.21, 2000.

B. Kedem and M. Frey, Time Series Analysis by Higher Order Crossings, 1934.

D. Keltner, P. Ekman, G. C. Gonzaga, and J. Beer, Facial expression of emotion, p.27, 2003.

L. Kessous, G. Castellano, and G. Caridakis, Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis, Journal on Multimodal User Interfaces, vol.3, issue.1, p.30, 2010.

S. Khalfa, I. Peretz, J. Blondin, and M. Robert, Event-related skin conductance responses to musical emotions in humans, Neuroscience Letters, vol.328, issue.2, p.24, 2002.

Z. Khalili, Emotion recognition system using brain and peripheral signals : using correlation dimension to improve the results of EEG, Proceedings of International Joint Conference on Neural Networks, vol.28, p.35, 2009.

Z. Khalili and M. H. Moradi, Emotion detection using brain and peripheral signals, Cairo International Biomedical Engineering Conference (CIBEC'08), p.22, 2008.

M. Masood-mehmood-khan, R. D. Ingleby, and . Ward, Automated Facial Expression Classification and affect interpretation using infrared measurement of facial skin temperature variations, ACM Transactions on Autonomous and Adaptive Systems, vol.1, issue.1, p.24, 2006.

J. Kim and E. Andre, Emotion recognition using physiological and speech signal in short-term observation, Perception and Interactive Technologies, vol.29, p.31, 2006.

J. Kim and E. André, Emotion recognition based on physiological changes in music listening, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.12, p.67, 1926.

A. B. Xxii and . Bibliographie,

S. W. Kyung-hwan-kim, S. R. Bang, and . Kim, Emotion recognition system using short-term monitoring of physiological signals, Medical and Biological Engineering and Computing, vol.42, issue.3, p.26, 2004.

Y. E. Kim, E. M. Schmidt, R. Migneco, B. G. Morton, P. Richardson et al., Music Emotion Recognition : a State of the Art Review, Information Retrieval, p.27, 2010.

R. Paul, A. M. Kleinginna, and . Kleinginna, A categorized list of emotion definitions, with suggestions for a consensual definition, Motivation and Emotion, vol.5, issue.4, p.18, 1981.

A. Kleinsmith and N. Bianchi-berthouze, Affective body expression perception and recognition : A survey, IEEE Transactions on Affective Computing, vol.4, issue.1, p.25, 2013.

S. Koelsch, Towards a neural basis of music-evoked emotions, Trends in Cognitive Sciences, vol.14, issue.3, p.48, 2010.

S. Koelstra, A. Yazdani, M. Soleymani, and C. Mühl, Single trial classification of EEG and peripheral physiological signals for recognition of emotions induced by music videos, Brain informatics, vol.6334, p.90, 2010.

S. Koelstra, A. Yazdani, M. Soleymani, C. Mühl, J. Lee et al., Single Trial Classification of EEG and Peripheral Physiological Signals for Recognition of Emotions Induced by Music Videos, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, vol.30, p.35, 2010.

S. Koelstra, C. Mühl, M. Soleymani, J. Lee, A. Yazdani et al., Thierry Pun, Anton Nijholt, and Ioannis (Yiannis) Patras. Deap : A database for emotion analysis using physiological signals, IEEE Transactions on Affective Computing, vol.3, issue.1, pp.18-31, 2012.

S. D. Kreibig, Autonomic nervous system activity in emotion : a review, Biological psychology, vol.84, issue.3, p.24, 2010.

J. Krimphoff, Analyse acoustique et perception du timbre [Acoustic analysis and perception of timbre, p.92, 1993.

J. Krimphoff, S. Mcadams, and S. Winsberg, Caracterisation du timbre de sons complexes : {II}. Analyses acoustiques et quantification psychophysique, Journal de Physique, vol.4, p.93, 1994.

T. Ludmila-i-kuncheva, I. Christy, . Pierce, P. Sa'ad, and . Mansoor, Multi-modal Biometric Emotion Recognition Using Classifier Ensembles, Modern Approaches in Applied Intelligence, number 6703, p.49, 2011.

A. B. Xxiii and . Bibliographie,

J. D. Laird, The real role of facial response in the experience of emotion : A reply to Tourangeau and Ellsworth, and others, Journal of Personality and Social Psychology, vol.47, issue.4, p.49, 1984.

J. D. Laird and S. Strout, Emotional behaviors as Emotional Stimuli. Handbook of Emotion Elicitation and Assesment, p.49, 2007.

J. A. Lambie and A. J. Marcel, Consciousness and the varieties of emotion experience : a theoretical framework, Psychological review, vol.109, issue.2, p.48, 2002.

J. , R. Landis, and G. G. Koch, The measurement of observer agreement for categorical data, Biometrics, vol.33, issue.1, p.77, 1977.

J. Peter and . Lang, The cognitive psychophysiology of emotion : Fear and anxiety. Anxiety and the anxiety disorders, p.45, 1985.

J. Peter, M. Lang, M. M. Greenwald, A. O. Bradley, and . Hamm, Looking at pictures : affective, facial, visceral, and behavioral reactions, Psychophysiology, vol.30, issue.3, p.49, 1993.

J. Peter, M. M. Lang, B. N. Bradley, and . Cuthbert, International Affective Picture System (IAPS) : Technical Manual and Affective Ratings. NIMH Center for the Study of Emotion and Attention, vol.49, p.50, 1997.

J. Peter, M. M. Lang, B. N. Bradley, and . Cuthbert, International affective picture system (IAPS) : Affective ratings of pictures and instruction manual, vol.60, p.94, 2008.

J. Ledoux, The amygdala, Current Biology, vol.17, issue.20, p.19, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00634626

J. E. Ledoux, Emotion circuits in the brain, Annual review of neuroscience, vol.23, p.20, 2000.

N. Leveau, S. Jhean-larose, and G. Denhière, EMOVAL : évaluation automatique de la valence et de l'activation émotionnelles des textes à l'aide d'une méta-norme de 5656 mots-racines, Psychologie française, vol.56, issue.4, p.42, 2011.

R. W. Levenson, Emotion and the autonomic nervous system : a prospectus for research on autonomic specificity, vol.24, p.50, 1988.

R. W. Levenson, Blood, Sweat, and Fears : The Autonomic Architecture of Emotion, Annals of the New York Academy of Sciences, vol.1000, issue.1, pp.348-366, 1924.

R. W. Levenson, The Autonomic Nervous System and emotion, Emotion Review, vol.6, issue.2, p.24, 2014.

M. Lewis, The Self-Conscious Emotions. Emotions, p.48, 2011.

A. B. Xxiv and . Bibliographie,

L. Li and J. Chen, Emotion recognition using physiological signals, Advances in Artificial Reality and Tele-Existence, p.26, 2006.

M. Li and B. Lu, Emotion classification based on gamma-band EEG, Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society : Engineering the Future of Biomedicine, vol.28, p.99, 2009.

C. Yuan-pin-lin, T. Wang, S. Wu, J. Jeng, and . Chen, Support vector machine for EEG signal classification during listening to emotional music

, Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing (MMSP 2008), vol.28, p.49, 2008.

C. Yuan-pin-lin, T. Wang, T. Jung, S. Wu, . Jeng et al., EEG-based emotion recognition in music listening, IEEE Transactions on Biomedical Engineering, vol.57, issue.7, p.49, 2010.

S. Liu, J. Meng, D. Zhang, J. Yang, X. Zhao et al., Emotion recognition based on EEG changes in movie viewing, International IEEE/EMBS Conference on Neural Engineering, vol.35, p.49, 2015.

H. Lux, Neurophysiological Basis of the EEG and DC Potentials, Electroencephalography : Basic Principles, Clinical Applications, and Related Fields, pp.24-28, 1981.

C. Ma and G. Liu, Feature Extraction, Feature Selection and Classification from Electrocardiography to Emotions, Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing, p.24, 2009.

C. Maaoui and A. Pruski, Emotion recognition through physiological signals for human-machine communication, Cutting Edge Robotics, p.26, 2010.

P. D. Maclean, Psychosomatic disease and the visceral brain, Psychosomatic Medicine, issue.6, p.20, 1949.

J. Makhoul, Linear prediction : A tutorial review, Proceedings of the IEEE, vol.63, issue.4, p.93, 1975.

A. Marchewka, L. Zurawski, K. Jednoróg, and A. Grabowska, The Nencki Affective Picture System (NAPS) : Introduction to a novel, standardized, wide-range, high-quality, realistic picture database, Behavior Research Methods, vol.46, issue.2, p.49, 2014.

S. Mariooryad and C. Busso, Analysis and Compensation of the Reaction Lag of Evaluators in Continuous Emotional Annotations, Humaine Association Conference on Affective Computing and Intelligent Interaction, pp.85-90, 2013.

O. Martin, I. Kotsia, B. Macq, and I. Pitas, The eNTERFACE'05 audiovisual emotion database, International Conference on Data Engineering Workshops, p.31, 2006.

D. S. Massey, A brief history of human society : The origin and role of emotion in social life, American Sociological Review, vol.67, p.25, 2002.

R. A. Mcfarland, Relationship of Skin Temperature Changes to the Emotions Accompanying Music, Biofeedback and Self-Regulation, vol.10, issue.3, p.24, 1985.

A. Mehrabian, Comparison of the PAD and PANAS as Models for Describing Emotions and for Differentiating Anxiety from Depression, Journal of Psychopathology and Behavioral Assessment, vol.19, issue.4, p.44, 1997.

A. Mehrabian and J. A. Russell, An approach to environmental psychology, vol.40, p.74, 1974.

A. Metallinou and S. Narayanan, Annotation and processing of continuous emotional attributes : Challenges and opportunities, 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013, number EmoSPACE, p.47, 2013.

J. D. Morris, Observations : SAM : The Self-Assessment Manikin -An Efficient Cross-Cultural Measurement of Emotional Response, Journal of Advertising Research, vol.35, issue.6, p.45, 1995.

C. Mühl, L. Egon, . Van-den, A. M. Broek, F. Brouwer et al., Multi-modal affect induction for affective brain-computer interfaces, Affective Computing and Intelligent Interaction, p.49, 2011.

C. Fionnuala, I. Murphy, A. Nimmo-smith, and . Lawrence, Functional neuroanatomy of emotions : a meta-analysis. Cognitive, affective and behavioral neuroscience, vol.3, p.20, 2003.

A. Nakasone, H. Prendinger, and M. Ishizuka, Emotion Recognition from Electromyography and Skin Conductance, The 5th International Workshop on Biosignal Interpretation, p.24, 2005.

J. Nicolle, V. Rapp, K. Bailly, L. Prevost, and M. Chetouani, Robust continuous prediction of human emotions using multiscale dynamic cues, Proceedings of the 14th ACM international conference on Multimodal interaction, p.47, 2012.
URL : https://hal.archives-ouvertes.fr/hal-02423171

D. Nie, L. C. Wang, and . Shi, EEG-based emotion recognition during watching movies, Neural Engineering (NER), p.28, 2011.

A. B. Xxvi and . Bibliographie,

S. W. Tin-lay-nwe, L. Foo, and . Silva, Speech emotion recognition using hidden Markov models, Speech Communication, vol.41, issue.4, pp.603-623, 2003.

P. Oudeyer, The production and recognition of emotions in speech : Features and algorithms, International Journal of Human Computer Studies, vol.59, issue.1-2, pp.157-183, 2003.
URL : https://hal.archives-ouvertes.fr/hal-00818196

M. Paleari, R. Chellali, and B. Huet, Features for multimodal emotion recognition : An extensive study, 2010 IEEE Conference on Cybernetics and Intelligent Systems (CIS), vol.29, p.31, 2010.

M. , C. Pastor, M. M. Bradley, A. Löw, and F. Versace,

J. Peter and . Lang, Affective picture perception : emotion, context, and the late positive potential, Brain research, vol.1189, p.48, 2008.

S. Paulmann and M. D. Pell, Is there an advantage for recognizing multi-modal emotional stimuli ? Motivation and Emotion, vol.35, p.49, 2011.

G. Peeters, A large set of audio features for sound description (similarity and classification) in the CUIDADO project, IRCAM, p.91, 2004.

G. Peeters, L. Bruno, P. Giordano, and . Susini, Nicolas Misdariis, and Stephen McAdams. The Timbre Toolbox : extracting audio descriptors from musical signals. The Journal of the, vol.130, p.92, 2011.

C. Pelachaud, Emotion-Oriented Systems, 2013.

L. Pessoa and R. Adolphs, Emotion processing and the amygdala : from a 'low road' to 'many roads' of evaluating biological significance, Nature reviews neuroscience, vol.11, issue.11, p.19, 2010.

T. Pfister, X. Li, G. Zhao, and M. Pietikainen, Recognizing Spontaneous Facial Micro-Expressions, IEEE International Conference on Computer Vision, p.25, 2011.

P. Philippot, Inducing and assessing differentiated emotion-feeling states in the laboratory, Cognition & Emotion, vol.48, p.49, 1993.

R. W. Picard, E. Vyzas, and J. Healey, Toward machine emotional intelligence : Analysis of affective physiological state, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.10, p.99, 2001.

A. Piolat and R. Bannour, EMOTAIX : Un scénario de Tropes pour l'identification automatisée du lexique émotionnel et affectif, Annee Psychologique, vol.109, issue.4, p.38, 2009.

R. Plutchik, ;. Ix-xxvii, and A. B. Bibliographie, Emotions : A general psychoevolutionary theory. Approaches to emotion, pp.197-219, 1984.

S. Polikovsky, Y. Kameda, and Y. Ohta, Facial micro-expressions recognition using high speed camera and 3D-gradient descriptor, 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP 2009), p.25, 2009.

J. Posner, J. A. Russell, and B. S. Peterson, The circumplex model of affect : an integrative approach to affective neuroscience, cognitive development, and psychopathology, Development and Psychopathology, vol.17, issue.3, p.44, 2005.

P. Rainville, A. Bechara, N. Naqvi, and A. R. Damasio, Basic emotions are associated with distinct patterns of cardiorespiratory activity, International Journal of Psychophysiology, vol.61, issue.1, p.24, 2006.

. Srinivasan-ramakrishnan, Speech Enhancement, Modeling And Recognition -Algorithms And Applications, p.27, 2012.

P. Rani, C. Liu, N. Sarkar, and E. Vanman, An empirical study of machine learning techniques for affect recognition in human-robot interaction, Pattern Analysis and Applications, vol.9, issue.1, p.26, 2006.

R. Reisenzein, The Schachter Theory of Emotion : Two Decades Later, Psychological Bulletin, vol.94, issue.2, p.16, 1983.

B. Rimé and K. R. Scherer, Les émotions, textes de base en psychologie, p.14, 1989.

S. E. Rimm-kaufman and J. Kagan, The psychological significance of changes in skin temperature, Motivation and Emotion, vol.20, issue.1, p.24, 1996.

J. Rottenberg, R. D. Ray, and J. J. Gross, Emotion Elicitation Using Films, Handbook of Emotion Elicitation and Assessment, vol.49, p.54, 2007.

J. A. Russell, A circumplex model of affect, Journal of Personality & Social Psychology, vol.39, issue.6, p.74, 1980.
URL : https://hal.archives-ouvertes.fr/hal-01086372

J. A. Russell and L. F. Barrett, Core affect, prototypical emotional episodes, and other things called emotion : dissecting the elephant, Journal of personality and social psychology, vol.76, issue.5, pp.805-819, 1999.

J. A. Russell and A. Mehrabian, Evidence for a three-factor theory of emotions, Journal of Research in Personality, vol.11, issue.3, p.40, 1977.

J. A. Russell and G. Pratt, A description of the affective quality attributed to environments, Journal of Personality and Social Psychology, vol.38, issue.2, p.42, 1980.

M. Sarlo, G. Buodo, S. Poli, and D. Palomba, Changes in EEG alpha power to different disgust elicitors : The specificity of mutilations, Neuroscience Letters, vol.382, issue.3, pp.291-296, 1929.

J. Sartre, Esquisse d'une théorie des émotions. Hermann, vol.16, p.17, 1965.

A. B. Xxviii and . Bibliographie,

A. Savran, K. Ciftci, G. Chanel, J. C. Mota, L. H. Viet et al., Emotion Detection in the Loop from, Brain Signals and Facial Images. eNTERFACE, vol.06, pp.69-80, 1997.

K. Schaaff and T. Schultz, Towards emotion recognition from electroencephalographic signals, 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, p.35, 2009.

K. Schaaff and T. Schultz, Towards an EEG-based emotion recognizer for humanoid robots, Proceedings -IEEE International Workshop on Robot and Human Interactive Communication, vol.6, p.35, 2009.

J. Schachter, Pain, Fear, and Anger in Hypertensives and Normotensives : A Psychophysiological Study, vol.19, p.14, 1957.

S. Schachter and J. Singer, Cognitive, social, and physiological determinants of emotional state, Psychological review, vol.69, issue.5, p.48, 1962.

K. R. Scherer, Psychological models of emotion. The neuropsychology of emotion, 2000.

K. R. Scherer, Which Emotions Can be Induced by Music ? What Are the Underlying Mechanisms ? And How Can We Measure Them ?, Journal of New Music Research, vol.33, issue.3, pp.239-251, 2004.

K. R. Scherer, Unconscious Processes in Emotion : The Bulk of the Iceberg. The unconscious in emotion, vol.18, pp.312-334, 2005.

K. R. Scherer, What are emotions ? And how can they be measured ?, Social Science Information, vol.44, issue.4, p.47, 2005.

K. R. Scherer, A. Schorr, and T. Johnstone, Appraisal processes in emotion : Theory, methods, research, vol.18, p.48, 2001.

R. Klaus, T. Scherer, E. Bänziger, and . Roesch, A Blueprint for Affective Computing : A sourcebook and manual, p.18, 2010.

K. Schindler, L. Van-gool, and B. De-gelder, Recognizing emotions expressed by body pose : A biologically inspired neural model, Neural Networks, vol.21, issue.9, pp.1238-1246, 1925.

A. Schlögl, The Electroencephalogram and the Adaptive Autoregressive Model : Theory and Applications, p.19, 2000.

B. Schuller, M. Lang, and G. Rigoll, Multimodal emotion recognition in audiovisual communication, Proceedings -2002 IEEE International Conference on Multimedia and Expo, vol.1, p.31, 2002.

A. B. Xxix and . Bibliographie,

B. Schuller, A. Batliner, S. Steidl, and D. Seppi, Recognising realistic emotions and affect in speech : State of the art and lessons learnt from the first challenge, Speech Communication, vol.53, issue.9, p.27, 2011.

J. Scott, Animal behaviour. Animal Behaviour, p.38, 1958.

N. Sebe, I. Cohen, T. Gevers, and T. S. Huang, Emotion recognition based on joint visual and audio cues, Proceedings -International Conference on Pattern Recognition, vol.1, p.31, 2006.

M. Shreve, S. Godavarthy, D. Goldgof, and S. Sarkar, Macro-and micro-expression spotting in long videos using spatio-temporal strain, 2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, FG 2011, p.25, 2011.

M. Siemer, I. B. Mauss, and J. J. Gross, Same situation -different emotions : how appraisals shape our emotions, Emotion, vol.7, issue.3, p.48, 2007.

C. Liyanage, T. Silva, R. Miyasato, and . Nakatsu, Facial emotion recognition using multi-modal information, Proceedings of IEEE International Conference on Information, Communications and Signal Processing (ICICS1997), vol.1, p.31, 1997.

H. Singh, M. Bauer, W. Chowanski, Y. Sui, D. Atkinson et al., The brain's response to pleasant touch : an EEG investigation of tactile caressing, Frontiers in human neuroscience, vol.8, p.49, 2014.

A. Tauseef-sohaib, S. Qureshi, and J. Hagelbäck, Olle Hilborn, and Petar Jercic. Evaluating classifiers for emotion recognition using EEG, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol.8027, p.35, 2013.

M. Soleymani, S. Koelstra, I. Patras, and T. Pun, Continuous emotion detection in response to music videos, 2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, p.47, 2011.

M. Soleymani, J. Lichtenauer, T. Pun, and M. Pantic, A multimodal database for affect recognition and implicit tagging, IEEE Transactions on Affective Computing, vol.3, issue.1, p.59, 2012.

M. Soleymani, M. Pantic, and T. Pun, Multimodal emotion recognition in response to videos, IEEE Transactions on Affective Computing, vol.3, issue.2, p.31, 2012.

L. R. Squire, Memory and the hippocampus : a synthesis from findings with rats, monkeys, and humans, Psychological review, vol.99, issue.2, p.19, 1992.

A. B. Xxx and . Bibliographie,

G. Stemmler, M. Heldmann, C. A. Pauls, and T. Scherer, Constraints for emotion specificity in fear and anger : the context counts, Psychophysiology, vol.38, issue.2, p.48, 2001.

C. L. Stephens, I. C. Christie, and B. H. Friedman, Autonomic specificity of basic emotions : Evidence from pattern classification and cluster analysis, Biological Psychology, vol.84, issue.3, p.24, 2010.

K. Takahashi, Remarks on emotion recognition from multi-modal bio-potential signals, Proceedings of the 2004 IEEE International Workshop on Robot and Human Interactive Communication, p.26, 2004.

K. Takahashi and A. Tsukaguchi, Remarks on Emotion Recognition from Multi-Modal Bio-Potential Signals, 2nd International Conference on Autonomous Robots and Agents, p.28, 2004.

S. S. Tomkins and R. Carter, What and where are the primary affects ? Some evidence for a theory, Perceptual and Motor Skills, vol.18, p.41, 1964.

P. Khiet, D. A. Truong, M. A. Van-leeuwen, and . Neerincx, Unobtrusive multimodal emotion detection in adaptive interfaces : speech and facial expressions, Foundations of Augmented Cognition, p.29, 2007.

N. Tsuchiya and R. Adolphs, Emotion and consciousness, Trends in cognitive sciences, vol.11, issue.4, p.48, 2007.

S. Valenzi, T. Islam, P. Jurica, and A. Cichocki, Individual Classification of Emotions Using EEG, Journal of Biomedical Science and Engineering, vol.7, p.35, 2014.

J. Van-hooff, A structural analysis of the social behavior of a semi-captive group of chimpanzees. Social communication and movement : Studies of interaction and expression in man and chimpanzee, p.38, 1973.

A. Vanpé, Expressions et micro-expressions spontanées de la face et de la voix en Interaction Homme-Machine : esquisse d'un modèle du" Feeling of Thinking, vol.25, p.29, 2011.

D. Ververidis and C. Kotropoulos, Emotional speech recognition : Resources, features, and methods, Speech Communication, vol.48, issue.9, p.27, 2006.

J. Wagner, J. Kim, and E. André, From physiological signals to emotions : Implementing and comparing selected methods for feature extraction and classification, IEEE International Conference on Multimedia and Expo, ICME 2005, p.26, 2005.

J. Wagner, F. Lingenfelser, E. André, and J. Kim, Exploring fusion methods for multimodal emotion recognition with missing data, IEEE Transactions on Affective Computing, vol.2, issue.4, p.31, 2011.

B. A. Wandell, S. O. Dumoulin, and A. A. Brewer, Visual field maps in human cortex, Neuron, vol.56, issue.2, p.21, 2007.

S. Wang, Y. Zhu, G. Wu, and Q. Ji, Hybrid video emotional tagging using users' EEG and video content. Multimedia tools and applications, p.27, 2014.

X. Wang, D. Nie, and B. Lu, EEG-based emotion recognition using frequency domain features and support vector machines, ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing, vol.29, p.35, 2011.

X. Wang, D. Nie, and B. Lu, Emotional state classification from EEG data using machine learning approach, Neurocomputing, vol.129, p.49, 2014.

A. B. Warriner, V. Kuperman, and M. Brysbaert, Norms of valence, arousal, and dominance for 13,915 English lemmas, Behavior research methods, vol.45, issue.4, p.42, 2013.

E. L. White, Cortical circuits : synaptic organization of the cerebral cortex : structure, function, and theory, p.19, 1989.

I. Winkler, . Jäger, T. Mihajlovi?, and . Tsoneva, Frontal EEG asymmetry based classification of emotional valence using common spatial patterns, World Academy of Science, Engineering and Technology, vol.45, p.90, 2010.

M. Wöllmer, F. Eyben, S. Reiter, B. Schuller, C. Cox et al., Abandoning emotion classes -Towards continuous emotion recognition with modelling of long-range dependencies, Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, p.47, 2008.

H. Xu and N. Konstantinos, Kostas) Plataniotis. Affect recognition using EEG signal, IEEE 14th International Workshop on Multimedia Signal Processing (MMSP), pp.299-304, 1997.

Q. Wen-jing-yan, J. Wu, Y. H. Liang, X. Chen, and . Fu, How Fast are the Leaked Facial Expressions : The Duration of Micro-Expressions, Journal of Nonverbal Behavior, vol.37, issue.4, pp.217-230, 1925.

M. Yik, J. H. James-a-russell, and . Steiger, A 12-point circumplex structure of core affect, Emotion, vol.11, issue.4, p.44, 2011.

A. B. Xxxii and . Bibliographie,

. Chai-tong-yuen, M. Woo-san-san, T. C. Rizon, and . Seong, Classification of Human Emotions from EEG Signals using Statistical Features and Neural Network, vol.1, p.49, 2011.

Z. Zeng and M. Pantic, A survey of affect recognition methods : Audio, visual, and spontaneous expressions, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.1, p.27, 2009.

C. Zong and M. Chetouani, Hilbert-Huang transform based physiological signals analysis for emotion recognition, IEEE Symposium on Signal Processing and Information Technology (ISSPIT), p.26, 2009.
URL : https://hal.archives-ouvertes.fr/hal-02423458