1. { NeuroPhone: Brain-Mobile Phone Interface using a Wireless EEG Headset Andrew T. Campbell, Tanzeem Choudhury, Shaohan Hu, Hong Lu, Matthew K. Mukerjee ∗, Mashfiqui Rabbi, and Rajeev D. S. Raizada Dartmouth College, Hanover, NH, USA

2. Motivation

3.  Control your mobile device without touching or speaking  Make control easier in environment require less movement or silence  library  classroom The narrow version for mobile device

4.  Control many device in a more effortless way  Determent human emotion for many purpose The generalized version

5. Terminology

6.  Electroencephalography  Device that recording electrical activity of brain EEG

7.  P300 is a pattern of certain electrical brain activity  It is usually happened when people try to reacting to certain thing  Typical representation is the record of EEG have a “delay” for 300 to 600 ms P300

8.  A way to generalize data, to figure out a pattern. So that we can use the pattern to determent or predict new situation Machine Learning

9. Y = aX0 + bX1 + cX2 Classification Problem

10. Test Environment

11. iPhone

12. Emotiv EPOC EEG headset

13. Windows Laptop

14. Application

15. P300 Mode

17.  Similar to P300 mode, but user must winking their eyes Wink mode

18. Result

19. P300 Mode

20. Winking Mode

21.  The application take very little resource  Which means devices like smart phone could totally hand the brain control system  An acceptable Accuracy Shows

22.  Algorithm is not perfect yet  High battery consuming  EEG hardware not good enough Limitations

23.  Expensive  Very little company working in this field  A lot of noise in the information  Too big  Too ugly EEG

24. EEG

25. Conclusion

26.  It is a concept prove application  It proved that brain control on mobile device can be done  The compute unit is powerful enough already  The algorithm can be improve  EEG still need improve

27.  Question? Thank you