Brain computer interface
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Brain computer interface

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a ppt for my mtech seminar

a ppt for my mtech seminar

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Brain computer interface Brain computer interface Presentation Transcript

  • BCI Implementation For Blind Subjects using Signal Processing BY-Sanchita Singha
  • Contents  What is BCI?  How human brain works  How BCI works  Uses of BCI  Implementation  Constraints  Conclusion 11/23/2013 BCI Implementation 2
  • What is BCI • BCI-Brain Computer Interface • Direct communication pathway between the brain and an external device • Reads electrical signals from brain 11/23/2013 BCI Implementation 3
  • How BCI works 11/23/2013 BCI Implementation 4
  • Types of BCI 11/23/2013 BCI Implementation 5
  • Practical Use of BCI  People with spinal injuries  Targeted for people with paralysis  People with acquired blindness can get vision 11/23/2013 Jens Naumann, a man with acquired blindness BCI Implementation 6
  • How virtual eye works 11/23/2013 BCI Implementation 7
  • Flowchart 11/23/2013 BCI Implementation 8
  • Signal Acquisition 11/23/2013 BCI Implementation 9
  • Training 11/23/2013 BCI Implementation 10
  • Feature Extraction 11/23/2013 BCI Implementation 11
  • Pre-processing 11/23/2013 BCI Implementation 12
  • Constraints • EEGs measure tiny voltage potentials. The signal is weak and prone to interference. • Each neuron is constantly sending and receiving signals through a complex web of connections. There are chemical processes involved as well, which EEGs can't pick up on. • The equipment heavy & hence not portable. 11/23/2013 BCI Implementation 13
  • Conclusion • Enables people to communicate and control appliances with use of brain signals • Open gates for disabled people. • Numerous future applications 11/23/2013 BCI Implementation 14
  • • • • • • • • • • • • • • • • • • • References [1] C. Guger, A. Schlögl, C. Neuper, D. Walterspacher, T. Strein, and G. Pfurtscheller, “Rapid prototyping of an EEG-based brain-computer interface (BCI),” IEEE Trans. Neural Syst. Rehab. Eng., vol. 9, no. 1, pp. 49–58, 2001. [2] G. Pfurtscheller, R. Leeb, C. Keinrath, D. Friedman, C. Neuper, C. Guger, and M. Slater, “Walking from thought,” Brain Res., vol. 1071, no. 1, pp. 145–152, 2006. [3] N.J. Hill, T.N. Lal, M. Schroder, T. Hinterberger, B. Wilhelm, F. Nijboer, U. Mochty, G. Widman, C. Elger, B. Scholkopf, A. Kubler, and N. Birbaumer, “Classifying EEG and ECoG signals without subject training for fast BCI implementation: Comparison of nonparalyzed and completely paralyzed subjects,” IEEE Trans. Neural Syst. Rehab. Eng., vol. 14, pp. 183–186, June 2006. [4] N. Weiskopf, K. Mathiak, S.W. Bock, F. Scharnowski, R. Veit, W. Grodd, R. Goebel, and N. Birbaumer, “Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI),” IEEE Trans. Biomed. Eng., vol. 51, pp. 966–970, June 2004. [5] S.-S. Yoo, T. Fairneny, N.-K. Chen, S.-E. Choo, L.P. Panych, H. Park, S.-Y. Lee, and F.A. Jolesz, “Brain-computer interface using fMRI: Spatial navigation by thoughts,” Neuroreport, vol. 15, no. 10, pp. 1591–1595, 2004. [6] M. A. L. Nicolelis, “Actions from Thoughts,” Nature, vol. 409, pp.403-407, 2001 [7] X. Gao, X. Dignfeng, M. Cheng and S. Gao, “A BCI-based Environmental Controller for the Motion-Disabled,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, pp. 137-140, 2003 [8] J. R. Mill´an, P. W. Ferrez and A. Buttfield, “Non Invasive Brain Machine Interfaces - Final report,” IDIAP Research Institute - ESA, 2005 [9] J. D. Bayliss, “Use of the Evoked Potential P3 Component for Control in a Virtual Environment,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, pp. 113-116, 2003 [10] J. L. Sirvent, J. M. Azor´ın, E. Ia´n˜ez, E., A. U´ beda and E. Ferna´ndez, “P300-based Brain-Computer Interface for Internet Browsing,” IEEE International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS), pp. 615-622, 2010 [11] E. Ia´n˜ez, J. M. Azor´ın, A. U´ beda, J. M. Ferra´ndez and E. Ferna´ndez, “Mental Tasks-Based Brain–Robot Interface,” Robotics and Autonomous Systems, vol. 58(12), pp. 1238-1245, 2010 [12] G. Pfurtscheller and C. Neuper, “Motor Imagery and Direct Brain Computer Communication,” Proceedings of the IEEE, vol. 89, pp. 1123-1134, 2001 [13] F. Lotte, M. Congedo, A. L´ecuyer, F. Lamarche and B. Arnaldi, “A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces,” Journal of Neural Engineering, vol. 4, pp. 1-13, 2007 [14] F. Cincotti et al., “High-resolution EEG Techniques for Brain Computer Interface Applications,” Journal of Neuroscience Methods, vol. 167(1), pp. 31-42, 2008 [15] R. T. Lauer, P. H. Peckham, K. L. Kilgore, andW. J. Heetderks, “Applications of cortical signals to neuroprosthetic control: A critical review,” IEEE Trans. Rehab. Eng., vol. 8, pp. 205–208, June 2000. [16] G. Garcia, T. Ebrahimi, and J.-M. Vesin, “Classification of EEG signals in the ambiguity domain for brain-computer interface applications,” in IEEE Int. Conf. Digit. Sig. Proc., Santorini, Greece, vol. 1, 1-3 July 2002, pp. 301-305. [17] H. Ramoser, J. Muller-Gerking, and G. Pfurtscheller, “Optimal spatial filtering of single trial EEG during imagined hand movement,” IEEE Trans. Rehab. Eng., vol. 8, pp. 441–446, Dec. 2000. [18] B. Kotchoubey, D. Schneider, H. Schleichert, U. Strehl, C. Uhlmann, V. Blankenhorn, W. Froscher, and N. Birbaumer, “Self-regulation of slow cortical potentials in epilepsy: A retrial with analysis of influencing factors,” Epilepsy Res., vol. 25, no. 3, pp. 269–276, Nov. 1996. 11/23/2013 BCI Implementation 15
  • ~Thank You~ For your attention 11/23/2013 BCI Implementation 16
  • Any Queries?? 11/23/2013 BCI Implementation 17