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 (Samp...
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my.utp.eprints.109142013-12-16T23:48:09Z EEG Classification of Physiological Conditions in 2D/3D Environments Using Neural Network Mumtaz, Wajid Xia, Likun Mumtaz, Wajid Yasin, Mohd Azhar Mohd QA Mathematics 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 (SampEn), Composite permutation entropy index (CPEI), Approximate entropy (ApEn), Fractal dimension (FD), Hurst exponent (H) and Hjorth parameters (complexity and mobility). These features are decision variables for classification of physiological conditions: Eyes Open (EO), Eyes Closed (EC),Game Playing 2D (GP2D), Game playing 3D active (GP3DA)and Game playing 3D passive (GP3DP). Results show that the scheme can successfully classify the conditions with an accuracy of 88.9%. 2013-07 Conference or Workshop Item PeerReviewed application/pdf http://eprints.utp.edu.my/10914/1/11870472.pdf http://embc2013.embs.org/ Mumtaz, Wajid and Xia, Likun and Mumtaz, Wajid and Yasin, Mohd Azhar Mohd (2013) EEG Classification of Physiological Conditions in 2D/3D Environments Using Neural Network. In: 35th IEEE International Conference of Engineering in Medicine and Biology (EMBC), July 03-07, 2013, Osaka, Japan. http://eprints.utp.edu.my/10914/ |
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QA Mathematics Mumtaz, Wajid Xia, Likun Mumtaz, Wajid Yasin, Mohd Azhar Mohd EEG Classification of Physiological Conditions in 2D/3D Environments Using Neural Network |
| description |
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 (SampEn), Composite permutation entropy index (CPEI), Approximate entropy (ApEn), Fractal dimension (FD), Hurst exponent (H) and Hjorth parameters (complexity and mobility). These features are decision variables for classification of physiological conditions: Eyes Open (EO), Eyes Closed (EC),Game Playing 2D (GP2D), Game playing 3D active (GP3DA)and Game playing 3D passive (GP3DP). Results show that the scheme can successfully classify the conditions with an accuracy of 88.9%. |
| format |
Conference or Workshop Item |
| author |
Mumtaz, Wajid Xia, Likun Mumtaz, Wajid Yasin, Mohd Azhar Mohd |
| author_facet |
Mumtaz, Wajid Xia, Likun Mumtaz, Wajid Yasin, Mohd Azhar Mohd |
| author_sort |
Mumtaz, Wajid |
| title |
EEG Classification of Physiological Conditions in 2D/3D
Environments Using Neural Network |
| title_short |
EEG Classification of Physiological Conditions in 2D/3D
Environments Using Neural Network |
| title_full |
EEG Classification of Physiological Conditions in 2D/3D
Environments Using Neural Network |
| title_fullStr |
EEG Classification of Physiological Conditions in 2D/3D
Environments Using Neural Network |
| title_full_unstemmed |
EEG Classification of Physiological Conditions in 2D/3D
Environments Using Neural Network |
| title_sort |
eeg classification of physiological conditions in 2d/3d
environments using neural network |
| publishDate |
2013 |
| url |
http://eprints.utp.edu.my/10914/1/11870472.pdf http://embc2013.embs.org/ http://eprints.utp.edu.my/10914/ |
| _version_ |
1738655910817955840 |