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 …

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

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  • 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. 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. 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. System  Overview  
  • 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. 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. 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. Valence-­‐Arousal  Plane  
  • 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. Results  (1)  
  • 11. Results  (2)  
  • 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. 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. 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. 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. Thank  you!    Rafael  <rafael.ramirez@upf.edu>