SuperLectures.com

CLASSIFICATION BY WEIGHTING FOR SPATIO-FREQUENCY COMPONENTS OF EEG SIGNAL DURING MOTOR IMAGERY

Biosignal Processing

Full Paper at IEEE Xplore

Presented by: Hiroshi Higashi, Author(s): Hiroshi Higashi, Toshihisa Tanaka, Tokyo University of Agriculture and Technology, Japan

We propose a novel method for the classification of EEG signals during motor-imagery. For motor-imagery based brain computer interface (MI-BCI), a method called common spatial pattern (CSP), which finds spatial weights for electrodes, is effective, however CSP needs bandpass filtering as preprocessing. This paper addresses the problem to find parameters of the filter as well as the spatial weights. The filter is parameterize as weights for frequency spectra. Finding the optimal parameters is formulated as a constraint minimum variance problem. Then, the spatial and frequency weights are sought by alternately solving the generalized eigenvalue problem, and the cost function monotonically decreases by the alternative optimization. In our experiment of MI-BCI, the proposed method achieved maximum improvement by 6% in the classification accuracy over conventional methods.


  Speech Transcript

|

  Slides

Enlarge the slide | Show all slides in a pop-up window

0:00:16

  1. slide

0:00:39

  2. slide

0:01:36

  3. slide

0:02:47

  4. slide

0:04:47

  5. slide

0:05:24

  6. slide

0:05:50

  7. slide

0:06:53

  8. slide

0:07:44

  9. slide

0:08:12

 10. slide

0:08:40

 11. slide

0:09:26

 12. slide

0:10:01

 13. slide

0:10:35

 14. slide

0:11:03

 15. slide

0:11:33

 16. slide

0:12:42

 17. slide

0:14:35

 18. slide

0:15:05

 19. slide

0:16:18

 20. slide

0:16:41

 21. slide

0:16:58

 22. slide

0:17:13

 23. slide

0:17:51

 24. slide

0:18:09

 25. slide

  Comments

Please sign in to post your comment!

  Lecture Information

Recorded: 2011-05-27 10:30 - 10:50, Club H
Added: 21. 6. 2011 17:27
Number of views: 19
Video resolution: 1024x576 px, 512x288 px
Video length: 0:18:37
Audio track: MP3 [6.29 MB], 0:18:37