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
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.
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.