Motor imagery data classification for BCI application using wavelet packet feature extraction Hettiarachchi,IT, Nguyen,TT and Nahavandi,S 2014, Motor imagery data classification for BCI application using wavelet packet feature extraction. In Loo,CK, Yap,KS, Wong,KW, Teoh,A and Huang,K (ed), Neural information processing : 21st International Conference ICONIP 2014 Kuching, Malaysia, November 3-6, 2014 Proceedings, Part III, Springer, Berlin, Germany, pp.519-526, doi: 10.1007/978-3-319-12643-2_63. Attached Files Name Description MIMEType Size Downloads Title Motor imagery data classification for BCI application using wavelet packet feature extraction Author(s) Hettiarachchi,IT orcid.org/0000-0002-4220-0970 Nguyen,TT orcid.org/0000-0001-9709-1663 Nahavandi,S orcid.org/0000-0002-0360-5270 Title of book Neural information processing : 21st International Conference ICONIP 2014 Kuching, Malaysia, November 3-6, 2014 Proceedings, Part III Editor(s) Loo,CK Yap,KS Wong,KW Teoh,A Huang,K Publication date 2014 Series Lecture notes in computer science; v.8836 Chapter number 63 Total chapters 83 Start page 519 End page 526 Total pages 8 Publisher Springer Place of Publication Berlin, Germany Keyword(s) Brain-computer interface Fisher distance criterion Motor imagery data Receiver operating characteristic curve Wavelet packet decomposition Science & Technology Technology Computer Science, Artificial Intelligence Computer Science, Information Systems Computer Science, Theory & Methods Computer Science Summary The noninvasive brain imaging modalities have provided us an extraordinary means for monitoring the working brain. Among these modalities, Electroencephalography (EEG) is the most widely used technique for measuring the brain signals under different tasks, due to its mobility, low cost, and high temporal resolution. In this paper we investigate the use of EEG signals in brain-computer interface (BCI) systems. ISBN 9783319126425 ISSN 0302-9743 1611-3349 Language eng DOI 10.1007/978-3-319-12643-2_63 Field of Research 080108 Neural, Evolutionary and Fuzzy Computation Socio Economic Objective 920111 Nervous System and Disorders HERDC Research category B1 Book chapter ERA Research output type B Book chapter Copyright notice ©2014, Springer Persistent URL http://hdl.handle.net/10536/DRO/DU:30071657 Document type: Book Chapter Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 20 Mar 2015, 14:19:10 EST Thu, 16 Apr 2015, 14:24:07 EST Thu, 16 Apr 2015, 16:26:15 EST Thu, 16 Apr 2015, 16:37:40 EST Thu, 16 Apr 2015, 16:38:33 EST Fri, 24 Apr 2015, 11:34:01 EST Fri, 24 Apr 2015, 11:34:48 EST Tue, 19 May 2015, 14:22:55 EST Fri, 12 Jun 2015, 13:40:43 EST Thu, 31 Mar 2016, 13:52:51 EST Filtered Full Citation counts:   Cited 14 times in TR Web of Science Cited 20 times in Scopus Search Google Scholar Access Statistics: 586 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Thu, 16 Apr 2015, 14:24:07 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.