201004 - brain computer interaction

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This is our presentation of final project of HCI course about, "Brain - Computer Interface: Alignment of Perceived and Physiological Engagement".

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201004 - brain computer interaction

  1. 1. Brain Computer Interface Alignment of Perceived and Physiological Engagement Javier Gonzalez | M. Elena Chavez | Randy Miller | Ryan Brotman
  2. 2. 2
  3. 3. Outline   The context   The experiments   The method   Data Analysis   Conclusion   Future Work
  4. 4. 1. The Context Brain Computer Interface Javier Gonzalez | Maria Elena Chavez | Randy Miller | Ryan Brotman
  5. 5. What is inside my head ? Perception Cognition Emotion Action 5
  6. 6. Measuring Emotions Perception Cognition Emotion Action 6
  7. 7. The medium and the kind of action Perception Cognition Emotion Action 7
  8. 8. Reading the Brainwaves Understanding Training time (for Cognition) Maintaining the nodes dampness. Emotion Wireless frequency at 2.4GHz Cognition 8
  9. 9. 9
  10. 10. 2. The Experiments Reading Emotions Javier Gonzalez | Maria Elena Chavez | Randy Miller | Ryan Brotman
  11. 11. The experiment one 11
  12. 12. The experiment one 12
  13. 13. The Experiment Two Question: task medium How do variables of interaction influence the alignment of perceived engagement and physiological engagement? 13
  14. 14. The experiment Two Independent Variable 1: Task. How do people´s alignment of perceived engagement and physiological engagement response differ across different tasks? Independent Variable 2: Medium. How do people’s alignment of perceived engagement and physiological engagement response differ if activities are presented in a physical or digital medium? 14
  15. 15. Quasi-Experimental Design Solitaire Maze Navigation 15 Piece Puzzle (ordering) (spatial navigation) (problem solving) Dissonance Dissonance Dissonance Virtual Dissonance Dissonance Dissonance Physical Dissonance: Ratio between actual amount of time and perceived amount of time 15
  16. 16. Flow Channel 16
  17. 17. Participants Volunteer Sampling Pilot Study, N=6 Random ordering of task assignment 17
  18. 18. Expectations Activities with high levels of both engagement and frustration will have a negative correlation with dissonance. Versions of activities that engage more senses will have a negative correlation with dissonance. 18
  19. 19. 3. The Methods Brainwaves and surveys Javier Gonzalez | Maria Elena Chavez | Randy Miller | Ryan Brotman
  20. 20. Immersion Tendency Survey 20
  21. 21. Data collection We use the Emotiv® User model for: Engagement Samples every 250 ms 21
  22. 22. Post Task Survey 22
  23. 23. 4. Data Analysis What do your brain revels about you? Javier Gonzalez | Maria Elena Chavez | Randy Miller | Ryan Brotman
  24. 24. Data Volume RAW EEG data 128 samples/sec * 60 sec/min * 30 min * 16 data points/sample = 3.6M data points Emotiv® SDK and NeuroVault 4 samples/sec * 60 sec/min * 30 min * 5 data points/sample = 3.6K data points 24
  25. 25. Headset Output 0 10 20 30 40 50 60 70 80 90 100 1 39 77 115 153 191 229 267 305 343 381 419 457 495 533 Affective Data Output 571 609 647 685 Frame 723 761 799 837 875 913 951 989 1027 1065 1103 1141 1179 1217 1255 25 LTE FRU EXC ENG MED
  26. 26. Top 3 Task : Survey Ranking 6 5 # of participants 4 3 2 1 0 15 puzzle virtual 15 puzzle Robot maze Robot maze Solitaire virtual Solitaire physical physical virtual physical Task 26
  27. 27. Conclusions   Works well for emotional state measures.   Control was inconsistent between users.   Preliminary analysis suggests that the user perceived emotions matches the physiological data.   This is relevant because is one of the first times using both subjective and objective data to investigate engagement.   Our contribution to the area is the blending of psychology and neuroscience to explore experience design factors.
  28. 28. Future Work Brain versus Facial-Based Emotion detection 28

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