Brain computer interface

BCI Implementation For Blind Subjects using Signal Processing

BY--Sanchita Singha

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ContentsWhat is BCI?How human brain works How BCI works Uses of BCI Implementation Constraints Conclusion

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What is BCI

• BCI-Brain Computer Interface• Direct communication pathway between the brain and an external device• Reads electrical signals from brain

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How BCI works

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Types of BCI

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Jens Naumann, a man with acquired

blindness

People with spinal injuries

Targeted for people with paralysis

People with acquired blindness can get vision

Practical Use of BCI

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How virtual eye works

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Flowchart

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Signal Acquisition

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Training

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Feature Extraction

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Pre-processing

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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.

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Conclusion• Enables people to communicate and control

appliances with use of brain signals• Open gates for disabled people.• Numerous future applications

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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.

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• [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

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~Thank You~For your attention

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