Detec%ng	  Emo%on	  from	  EEG	  Signals	    Using	  the	  Emo%ve	  Epoc	  Device	                 	  Rafael	  Ramirez	  	...
Mo%va%on	  •  Study	  of	  emo%ons	  in	  human-­‐computer	     interac%on	  has	  increased	  in	  recent	  years	  •  Gr...
Contrib%ons	  •  Method	  for	  EEG-­‐based	  emo%on	  detec%on	  •  Use	  of	  low-­‐cost	  	  technology	  -­‐>	  Emo%v	...
System	  Overview	  
Data	  Collec%on	  •  6	  healthy	  subjects	  	  (mean	  age	  =	  30);	  listening	  to	  12	     (5-­‐10s	  long)	  emo...
Feature	  Extrac%on	  •  Alpha	  (8-­‐12Hz)	  and	  Beta	  (12-­‐30Hz)	  bands	  are	     par%cular	  bands	  of	  interes...
Classifica%on	  Learning	  Task	  •  Detect	  emo%onal	  state	  of	  mind	  of	  a	  person	     based	  on	  observed	  E...
Valence-­‐Arousal	  Plane	  
Algorithms	  •  Linear	  Discriminant	  Analysis	  (LDA)	  •  Support	  Vector	  Machines	  (SVM)	      –  linear	  kernel...
Results	  (1)	  
Results	  (2)	  
Results	  (3)	  •  Results	  indicate	  that	  the	  EEG	  data	  contains	     sufficient	  info	  to	  dis%nguish	  betwee...
Results	  (4)	  •  Inter-­‐subjects	  accuracy	  differences	  may	  be	     due	  to	  	     –  different	  degrees	  of	  ...
Conclusion	  •  Low-­‐cost	  emo%on	  detec%on	  system	  •  no	  self-­‐assessment	  informa%on	  about	  the	     emo%on...
Future	  work	  •  Improve	  classifica%on	  accuracy	      –  Systema%cally	  exploring	  different	  feature	         extr...
Thank	  you!	                      	  Rafael	  <rafael.ramirez@upf.edu>	  
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Brain inf2012(present)

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Presentation of Research by Rafael Ramirez at 2012 International Conference on Brain Informatics, 4-7 December 2012, Macau

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Brain inf2012(present)

  1. 1. Detec%ng  Emo%on  from  EEG  Signals   Using  the  Emo%ve  Epoc  Device    Rafael  Ramirez     Zacharias  Vamvakousis   Universitat  Pompeu  Fabra   Barcelona,  Spain     Presented  by:     Álvaro  Barbosa   University  of  Saint  Joseph   Macau  SAR,  China     Brain  Informa%cs  2012  
  2. 2. Mo%va%on  •  Study  of  emo%ons  in  human-­‐computer   interac%on  has  increased  in  recent  years  •  Growing  need  for  computer  applica%ons   capable  of  detec%ng  users’  emo%onal  state  •  Facial  and  voice  informa%on     –  can  be  consciously  controlled  and  modified   –  interpreta%on  is  oSen  subjec%ve  •   Here,  we  use  EEG-­‐based  emo%on  detec%on  
  3. 3. Contrib%ons  •  Method  for  EEG-­‐based  emo%on  detec%on  •  Use  of  low-­‐cost    technology  -­‐>  Emo%v  EPOC   headset  •  We  do  not  rely  in  subject  self-­‐reported  emo%onal   states  (as  most  previous  work  do)  •  Instead,  we  use  a  library  of  emo%on-­‐annotated   sounds     (IADS  Lib  -­‐  hp://csea.phhp.ufl.edu/media/iadsmessage.html)  
  4. 4. System  Overview  
  5. 5. Data  Collec%on  •  6  healthy  subjects    (mean  age  =  30);  listening  to  12   (5-­‐10s  long)  emo%on-­‐annotated  sounds  (IADS  Lib)  •  Emo%v  EPOC  headset  -­‐  14  data-­‐collec%ng  electrodes   (AF3,  F7,  F3,  FC5,  T7,  P7,  O1,  O2,  P8,  T8,  FC6,  F4,  F8  and  AF4)  and  2   reference  electrodes    
  6. 6. Feature  Extrac%on  •  Alpha  (8-­‐12Hz)  and  Beta  (12-­‐30Hz)  bands  are   par%cular  bands  of  interest  in  emo%on  research   for  both  valence  and  arousal  •  We  apply  bandpass  filtering  for  extrac%ng  alpha   and  beta  frequency  bands  •  EEG  signal  in  four  loca%ons  in  the  prefrontal   cortex:  AF3,  AF4,  F3  and  F4  •  Arousal  =  a(AF3+AF4+F3+F4)/b(AF3+AF4+F3+F4)  •  valence  =    aF4  /bF4  −    aF3  /bF3  
  7. 7. Classifica%on  Learning  Task  •  Detect  emo%onal  state  of  mind  of  a  person   based  on  observed  EEG  data  •  We  approach  this  problem  as  a  two  2-­‐class   classifica%on  problem   –  high/low  arousal     –  posi%ve/nega%ve  valence    ArousalClassif  ier  (  EEGdata([  t,  t  +c]))  →  {high,  low}    ValenceClassifier  (  EEGdata([  t,  t  +c]))  →  {posi%ve,  nega%ve}      c=1s  and  with  increments  of    t  of  0.0625s  
  8. 8. Valence-­‐Arousal  Plane  
  9. 9. Algorithms  •  Linear  Discriminant  Analysis  (LDA)  •  Support  Vector  Machines  (SVM)   –  linear  kernel   –  radial  basis  func%on  (RBF)  kernel  •  Evalua%on:    10-­‐fold  cross  valida%on  
  10. 10. Results  (1)  
  11. 11. Results  (2)  
  12. 12. Results  (3)  •  Results  indicate  that  the  EEG  data  contains   sufficient  info  to  dis%nguish  between  high/low   arousal  and  posiFve/negaFve  valence  states  •  Machine  learning  methods  are  capable  of   learning  the  EGG  paerns  that  dis%nguish   these  states  •  Different  accuracies  among  different  subjects  •  For  a  subject,  similar  accuracies  with  different     learning  method  
  13. 13. Results  (4)  •  Inter-­‐subjects  accuracy  differences  may  be   due  to     –  different  degrees  of  emo%onal  response  between   different  individuals,  or     –  amount  of  noise  for  different  subjects.    •  Anyway,  there  exists  considerable  varia%on  in   EEG  responses  among  different  subjects  
  14. 14. Conclusion  •  Low-­‐cost  emo%on  detec%on  system  •  no  self-­‐assessment  informa%on  about  the   emo%onal  states  by  the  subjects  •  linear  discriminant  analysis  and  support  vector   machines  classifica%on  •  Classifiers  able  to  discriminate  between  high-­‐ low  arousal  and  posi%ve-­‐nega%ve  valence  
  15. 15. Future  work  •  Improve  classifica%on  accuracy   –  Systema%cally  exploring  different  feature   extrac%on  methods  and  learning  methods  •  Incorpora%ng  self-­‐assessment  informa%on   would  very  likely  also  improve  the  accuracies   of  the  classifiers  
  16. 16. Thank  you!    Rafael  <rafael.ramirez@upf.edu>  

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