1 / 27

NeuroPhone : Brain-Mobile Phone Interface using a Wireless EEG Headset

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 . Motivation.

evan
Télécharger la présentation

NeuroPhone : Brain-Mobile Phone Interface using a Wireless EEG Headset

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. NeuroPhone: Brain-Mobile Phone Interface using a Wireless EEG Headset Andrew T. Campbell, TanzeemChoudhury, 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

  16. 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

More Related