Facial electromyography (fEMG) activities in response to affective visual stimulation

Recently, affective computing findings demonstrated that emotion processing and recognition is important in improving the quality of human computer interaction (HCI). In the present study, new data for a robust discrimination of three emotional states (negative, neutral and positive) employing two-channel facial electromyography (EMG) over zygomaticus major and corrugator supercilii will be presented. The facial EMG activities evoked upon viewing a standard set of pictures selected from the International Affective Picture System (IAPS) and additional self selected pictures revealed that positive pictures led to increased facial EMG activities over zygomaticus major (F (2, 471) = 4.23, p < 0.05), whereas negative pictures elicited greater facial EMG activities over corrugator supercilii (F (2, 476) = 3.06, p < 0.05). In addition, the correlation between facial EMG activities over these two sites and participants' ratings of stimuli pictures in dimension of valence measured by Self-Assessment Manikin (SAM) was significant (r = −0.63, p < 0.001, corrugator supercilii, r = 0.51, p < 0.05, zygomaticus major, respectively). Our results suggest that emotion inducing pictures elicit the intended emotions and that corrugator and zygomaticus EMG can effectively and reliably differentiate negative and positive emotions, respectively.

[1]  P. Lang.  International affective picture system (IAPS) : affective ratings of pictures and instruction manual , 2005 .

[2]  Charalampos Bratsas,et al.  Toward Emotion Aware Computing: An Integrated Approach Using Multichannel Neurophysiological Recordings and Affective Visual Stimuli , 2010, IEEE Transactions on Information Technology in Biomedicine.

[3]  M. Bradley,et al.  Affective reactions to acoustic stimuli. , 2000, Psychophysiology.

[4]  G. Klerman,et al.  Facial muscle patterning to affective imagery in depressed and nondepressed subjects , 1976, Science.

[5]  Maital Neta,et al.  Corrugator muscle responses are associated with individual differences in positivity-negativity bias. , 2009, Emotion.

[6]  Orlando Francisco Amodeo Bueno,et al.  IAPS includes photographs that elicit low-arousal physiological responses in healthy volunteers , 2007, Physiology & Behavior.

[7]  Fernando De la Torre,et al.  Detecting depression from facial actions and vocal prosody , 2009, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops.

[8]  Elisabeth André,et al.  Emotion recognition based on physiological changes in music listening , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  A. J. Fridlund,et al.  Guidelines for human electromyographic research. , 1986, Psychophysiology.

[10]  W. Davis,et al.  Properties of human affect induced by static color slides (IAPS): dimensional, categorical and electromyographic analysis , 1995, Biological Psychology.

[11]  J. Cacioppo,et al.  Relationship between attitudes and evaluative space: A critical review, with emphasis on the separability of positive and negative substrates. , 1994 .

[12]  K. Scherer,et al.  Sequential unfolding of novelty and pleasantness appraisals of odors: evidence from facial electromyography and autonomic reactions. , 2009, Emotion.

[13]  Bernhard Dahme,et al.  Gradients of Facial EMG and Cardiac Activity During Emotional Stimulation , 1999 .

[14]  P. Ekman,et al.  Facial signs of emotional experience. , 1980 .

[15]  M. Bradley,et al.  Looking at pictures: affective, facial, visceral, and behavioral reactions. , 1993, Psychophysiology.

[16]  Flávia Teixeira-Silva,et al.  The anxiogenic video-recorded Stroop Color–Word Test: psychological and physiological alterations and effects of diazepam , 2004, Physiology & Behavior.

[17]  J. Cacioppo,et al.  Specific forms of facial EMG response index emotions during an interview: from Darwin to the continuous flow hypothesis affect-laden information processing. , 1988, Journal of personality and social psychology.

[18]  M. Bradley,et al.  Emotion, attention, and the startle reflex. , 1990, Psychological review.

[19]  D. G. Laing,et al.  Facial electromyography: responses of children to odor and taste stimuli. , 2007, Chemical senses.

[20]  J. Larsen,et al.  A facial electromyographic investigation of affective contrast. , 2009, Psychophysiology.

[21]  U. Dimberg,et al.  Facial electromyographic reactions and autonomic activity to auditory stimuli , 1990, Biological Psychology.

[22]  Charalampos Bratsas,et al.  On the Classification of Emotional Biosignals Evoked While Viewing Affective Pictures: An Integrated Data-Mining-Based Approach for Healthcare Applications , 2010, IEEE Transactions on Information Technology in Biomedicine.

[23]  F. Schwab,et al.  Viewers Viewed : Facial Expression Patterns while Watching TV News , 2005 .

[24]  Dewen Hu,et al.  Globally Consistent Reconstruction of Ripped-Up Documents , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Jeff T. Larsen,et al.  Effects of positive and negative affect on electromyographic activity over zygomaticus major and corrugator supercilii. , 2003, Psychophysiology.

[26]  P. Ekman,et al.  Unmasking the face : a guide to recognizing emotions from facial clues , 1975 .

[27]  J. Cacioppo,et al.  The skeletomotor system: Surface electromyography. , 2007 .

[28]  L. Anolli.  The hidden structure of interaction : from neurons to culture patterns , 2005 .

[29]  A. Mehrabian.  Framework for a comprehensive description and measurement of emotional states. , 1995, Genetic, social, and general psychology monographs.

[30]  P. Lang.  Behavioral treatment and bio-behavioral assessment: computer applications , 1980 .

[31]  M. Bradley,et al.  The International Affective Picture System (IAPS) in the study of emotion and attention. , 2007 .