1 / 10

EEG-based Online Brain-Computer Interface System

EEG-based Online Brain-Computer Interface System. Chi-Ying Chen,Chang-Yu Tsai,Ya-Chun Tang Advisor:Yong-Sheng Chen. Outline. Introduction Motivation State of the Art Background Proposal Schedule Reference. Motivation. People with degenerative diseases Human-computer interface

lauren
Download Presentation

EEG-based Online Brain-Computer Interface System

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. EEG-based Online Brain-Computer Interface System Chi-Ying Chen,Chang-Yu Tsai,Ya-Chun Tang Advisor:Yong-Sheng Chen

  2. Outline • Introduction • Motivation • State of the Art • Background • Proposal • Schedule • Reference

  3. Motivation • People with degenerative diseases • Human-computer interface • Eye-tracking system • Voice-controlled interface • Brain-computer interface

  4. State of the art • BCI research • Challenges • Noise interference • Inter-/intra-subject variation • Asynchronous operation

  5. Background • BCI (Brain Computer Interface) • allow completely paralyzed people to communicate with the world by means of their brain wave BCI2000: A General-Purpose Brain-Computer Interface (BCI) System Gerwin Schalk*, Member, IEEE, Dennis J. McFarland, Thilo Hinterberger, Niels Birbaumer, and Jonathan R.Wolpaw

  6. Proposal

  7. Off-line training • Signal acquisition • Signal preprocessing • Feature extraction • Classifier training

  8. On-line testing • Asynchronous operation • Signal acquisition • Signal preprocessing • Feature extraction • Classification • Visual feedback • On-line training

  9. Schedule

  10. Reference • [1] E. A. Curran and M. J. Stokes, "Learning to control brain activity: a view of the production and control of EEG components for driving brain-computer interface (BCI) systems," Brain and Cognition, 51:326-336, 2003. • [2] G. Pfurtscheller, C. Neuper, D. Flotzinger, M. Pregenzer, "EEG-based discrimination between imagination of right and left hand movement," Electroencephalogr Clin Neurophysiol, 103(6):642-51, 1997. • [3]T. M. Vaughan, J. R. Wolpaw, and E. Donchin, "EEG-based communication: prospects and problems," IEEE Trans. Rehab. Eng., 4(4):425-430, 1996. • [4]H. Ramoser, J. Müller-Gerking, and G. Pfurtscheller, "Optimal spatial filtering of single trial EEG during imagined hand movement, " IEEE Trans. Rehab. Eng., 8(4):441-446, 2000. • [5] J. Kalcher, and G. Pfurtscheller, "Discrimination between phase-locked and non-phase-locked event-related EEG activity, " Electroenceph. clin. Neurophysiol. 94: 381-384, 1995 • [6]Y. Wang, Z. Zhang, Y. Li, X. Gao, S. Gao, Senior Member, IEEE, and F. Yang, "BCI competition 2003—data set IV: an algorithm based on CSSD and FDA for classifying single-trial EEG," IEEE Trans. on Biomedical Eng., 51(6), JUNE 2004 • [7]L. F. Chen, H. Y. M. Liao, M. T. Ko, J. C. Lin, and G. J. Yu, "A new LDA-based face recognition system which can solve the small sample size problem," Pattern Recognition, 33, 2000 • [8]J. Müller-Gerking, G. Pfurtscheller, and H. Flyvbjerg, "Designing optimal spatial filters for single-trial EEG classification in a movement task," Electroenceph. Clin. Neurophysiol., 110:787-798, 1999. • [9] J. R. Wolpaw, and D. J. McFarland, "Two-dimensional movement control by scalp-recorded sensorimotor rhythms in humans," Abstract Viewer/Itinerary Planner, Soc. Neuroscience Abstr., 2003.

More Related