• About
  • Policies
  • What is openaccess
  • Library
  • Contact
Advanced search
      View Item 
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
      • View Item
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Classification of multichannel ECoG related to individual finger movements with redundant spatial projections

      Thumbnail
      View / Download
      552.2 Kb
      Author
      Onaran, ibrahim
      İnce, N. Fırat
      Çetin, A. Enis
      Date
      2011
      Source Title
      2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
      Publisher
      IEEE
      Pages
      5424 - 5427
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
      170
      views
      128
      downloads
      Abstract
      We tackle the problem of classifying multichannel electrocorticogram (ECoG) related to individual finger movements for a brain machine interface (BMI). For this particular aim we applied a recently developed hierarchical spatial projection framework of neural activity for feature extraction from ECoG. The algorithm extends the binary common spatial patterns algorithm to multiclass problem by constructing a redundant set of spatial projections that are tuned for paired and group-wise discrimination of finger movements. The groupings were constructed by merging the data of adjacent fingers and contrasting them to the rest, such as the first two fingers (thumb and index) vs. the others (middle, ring and little). We applied this framework to the BCI competition IV ECoG data recorded from three subjects. We observed that the maximum classification accuracy was obtained from the gamma frequency band (65200Hz). For this particular frequency range the average classification accuracy over three subjects was 86.3%. These results indicate that the redundant spatial projection framework can be used successfully in decoding finger movements from ECoG for BMI. © 2011 IEEE.
      Keywords
      Brain machine interface
      Classification accuracy
      Common spatial patterns
      Electrocorticogram
      Finger movements
      Frequency ranges
      Multi-channel
      Multi-class problems
      Neural activity
      Algorithms
      Feature extraction
      Frequency bands
      Electrophysiology
      Permalink
      http://hdl.handle.net/11693/28248
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/IEMBS.2011.6091341
      Collections
      • Department of Computer Engineering 1368
      • Department of Electrical and Electronics Engineering 3524
      Show full item record

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartments

      My Account

      Login

      Statistics

      View Usage StatisticsView Google Analytics Statistics

      Bilkent University

      If you have trouble accessing this page and need to request an alternate format, contact the site administrator. Phone: (312) 290 1771
      Copyright © Bilkent University - Library IT

      Contact Us | Send Feedback | Off-Campus Access | Admin | Privacy