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BCI Web Browsing - PowerPoint PPT Presentation

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BCI Web Browsing
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  1. BCI Web Browsing AnshuRustagi Sam Liu Advisor: Abdullah Akce

  2. Overview • BCI – Brain Computer Interface • Uses EEG to receive brain signals • Goal: Use EEG to control a web browser • Problems: Many

  3. Presentation Outline • Problems with EEG/BCI • Implementation Methods • Using BCI to browse the web • Demo Video

  4. Problems with EEG/BCI • Very fuzzy signals (nearest neighbor search doesn’t work) • Varies per user, system needs to be trained to a specific user • Medical-grade EEG very expensive and is not designed for everyday use

  5. Emotiv • Originally designed for gaming • Consumer-grade (USB) • Uses wet electrode tech(saline solution) • Official SDK is expensive • Windows based: hacked libraries exist for linux but are not yet usable

  6. Implementing BCI • (3) primary methods • SSVEP • Motor Imagery • P300 • Fuzzy signals – even the most accurate method (motor imagery) for a given input is only 80% accurate • Subject Dependent – some people can’t use certain methods (they don’t work)

  7. P300 • Captures infrequent behavior • Produce different options, flash one randomly. User focuses on one, and when it flashes, there will be a peak in voltage. • We decided not to use this – difficult implementation and not good accuracy.

  8. SSVEP • “Steady state visually evoked potential” • Flash different options at different frequencies, we can capture which option the user looks at. Higher accuracy. • Example research: checkers played via robots controlled using BCI – the board was rigged with SSVEP • Problems: Still imperfect accuracy, also eye fatigue (limit ~30 minutes)

  9. Motor Imagery • The most accurate of our three methods • 1.5 bits of input (left, right, “nothing”) • “Imagine your left hand waving, imagine your right hand waving” • Example research: controlling a UAV with motor imagery • Problems: Small # inputs, same accuracy and usability problems (subject-dependent)

  10. Web Browsing with BCI (Ideas) • SSVEP • Theoretically we can pinpoint an object on the screen with 4 readings, with 92% certainty. • 4 LEDs on the corners of a computer screen • Motor Imagery/P300 • Two motor imagery inputs to navigate • Use a P300 speller

  11. Our Research • Uses Motor Imagery, P300 not implemented yet. • Current goal: Allow a user to navigate websites using hyperlinks • Written in Python a demo that uses mouse clicks left/right to cycle through links, and a third input will choose the link. • No speller yet – beyond scope of current goals • Problems with Emotiv headset (hacking efforts, etc)

  12. Python Demo Screenshots

  13. Iframed Browsing Site

  14. Accomplishments andFuture Improvements • We have a functional web browsing solution with motor imagery • Some sites (google news) don’t allow themselves to be iframed; change to client-side link aggregation • Emotiv headset laggy & can be hard to use – improve accuracy; hack for *nix? • Add a P300 Speller for URLs • Link importance algorithm for faster browsing

  15. Video

  16. Acknowledgements • Abdullah Akce for being a great mentor and guiding us through this research • Adeel Ahmad for lending us an Emotiv headset • Se-joon Chung for assistance and training with the Emotiv

  17. Questions?