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

1. Brain Computer Interface
Alignment of
Perceived and Physiological Engagement
Javier Gonzalez | M. Elena Chavez | Randy Miller | Ryan Brotman

2. 2

3. Outline
 The context
 The experiments
 The method
 Data Analysis
 Conclusion
 Future Work

4. 1. The Context
Brain Computer Interface
Javier Gonzalez | Maria Elena Chavez | Randy Miller | Ryan Brotman

5. What is inside my head ?
Perception
Cognition Emotion
Action
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6. Measuring Emotions
Perception
Cognition Emotion
Action
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7. The medium and the kind of action
Perception
Cognition Emotion
Action
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8. Reading the Brainwaves
Understanding
Training time (for Cognition)
Maintaining the nodes dampness. Emotion
Wireless frequency at 2.4GHz
Cognition
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9. 9

10. 2. The Experiments
Reading Emotions
Javier Gonzalez | Maria Elena Chavez | Randy Miller | Ryan Brotman

11. The experiment one
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12. The experiment one
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13. The Experiment Two
Question:
task
medium
How do variables of interaction influence the alignment of
perceived engagement and physiological engagement?
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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?
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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
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16. Flow Channel
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17. Participants
Volunteer Sampling
Pilot Study,
N=6
Random ordering
of task assignment
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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.
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19. 3. The Methods
Brainwaves and surveys
Javier Gonzalez | Maria Elena Chavez | Randy Miller | Ryan Brotman

20. Immersion Tendency Survey
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21. Data collection
We use the Emotiv®
User model for:
Engagement
Samples
every 250 ms
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22. Post Task Survey
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23. 4. Data Analysis
What do your brain revels about you?
Javier Gonzalez | Maria Elena Chavez | Randy Miller | Ryan Brotman

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
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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
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LTE
FRU
EXC
ENG
MED

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
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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. Future Work
Brain versus Facial-Based
Emotion detection
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