EEG classification of physiological conditions in 2D/3D environments using neural network
Higher classification accuracy is more desirable for brain computer interface (BCI) applications. The accuracy can be achieved by appropriate selection of relevant features. In this paper a new scheme is proposed based on six different nonlinear features. These features include Sample entropy (SampE...
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Main Authors: | , , , |
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Format: | Conference or Workshop Item |
Published: |
2013
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Subjects: | |
Online Access: | http://eprints.utp.edu.my/10826/1/EEG%20Classification%20of%20Physiological%20Conditions%20in%202D_3D.pdf http://dx.doi.org/10.1109/EMBC.2013.6610480 http://eprints.utp.edu.my/10826/ |
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Institution: | Universiti Teknologi Petronas |