Brain machine interface: classification of mental tasks using short-time PCA and recurrent neural networks
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Institute of Electrical and Electronics Engineering (IEEE)
2009
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my.unimap-74182010-11-24T03:01:17Z Brain machine interface: classification of mental tasks using short-time PCA and recurrent neural networks Hema, Chengalvarayan Radhakrishnamurthy Paulraj, Murugesapandian Sazali, Yaacob Abd Hamid, Adom Ramachandran, Nagarajan hema@unimap.edu.my Brain-computer interfaces Electroencephalography Medical signal processing Signal classification EEG signal classification Feature extraction Link to publisher's homepage at http://ieeexplore.ieee.org Brain machine interface provides a communication channel between the human brain and an external device. Brain interfaces are studied to provide rehabilitation to patients with neurodegenerative diseases; such patients loose all communication pathways except for their sensory and cognitive functions. One of the possible rehabilitation methods for these patients is to provide a brain machine interface (BMI) for communication, using the electrical activity of the brain detected by scalp EEG electrodes. Classification of EEG signals extracted during mental tasks is a technique for designing a BMI. In this paper a BMI design using five mental task EEG signals from two subjects were studied, a combination of two tasks is studied per subject. An Elman recurrent neural network is proposed for classification of EEG signals. Principal component analysis is used for extracting features from the EEG signals. The EEG signal is classified into two tasks. Ten such task combinations are studied. Average classification accuracies varied from 75.5% to 100% with a testing error tolerance of 0.05. The classification performance of the proposed algorithm is found to be better compared to our earlier work using AR model features. 2009-12-14T08:25:47Z 2009-12-14T08:25:47Z 2007-11-25 Working Paper p.1153-1156 978-1-4244-1355-3 http://hdl.handle.net/123456789/7418 http://ieeexplore.ieee.org/xpls/abs_all.jsp?=&arnumber=4658565 en Proceedings of the International Conference on Intelligent and Advanced Systems (ICIAS 2007) Institute of Electrical and Electronics Engineering (IEEE) |
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English |
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Brain-computer interfaces Electroencephalography Medical signal processing Signal classification EEG signal classification Feature extraction |
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Brain-computer interfaces Electroencephalography Medical signal processing Signal classification EEG signal classification Feature extraction Hema, Chengalvarayan Radhakrishnamurthy Paulraj, Murugesapandian Sazali, Yaacob Abd Hamid, Adom Ramachandran, Nagarajan Brain machine interface: classification of mental tasks using short-time PCA and recurrent neural networks |
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Link to publisher's homepage at http://ieeexplore.ieee.org |
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hema@unimap.edu.my |
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hema@unimap.edu.my Hema, Chengalvarayan Radhakrishnamurthy Paulraj, Murugesapandian Sazali, Yaacob Abd Hamid, Adom Ramachandran, Nagarajan |
| format |
Working Paper |
| author |
Hema, Chengalvarayan Radhakrishnamurthy Paulraj, Murugesapandian Sazali, Yaacob Abd Hamid, Adom Ramachandran, Nagarajan |
| author_sort |
Hema, Chengalvarayan Radhakrishnamurthy |
| title |
Brain machine interface: classification of mental tasks using short-time PCA and recurrent neural networks |
| title_short |
Brain machine interface: classification of mental tasks using short-time PCA and recurrent neural networks |
| title_full |
Brain machine interface: classification of mental tasks using short-time PCA and recurrent neural networks |
| title_fullStr |
Brain machine interface: classification of mental tasks using short-time PCA and recurrent neural networks |
| title_full_unstemmed |
Brain machine interface: classification of mental tasks using short-time PCA and recurrent neural networks |
| title_sort |
brain machine interface: classification of mental tasks using short-time pca and recurrent neural networks |
| publisher |
Institute of Electrical and Electronics Engineering (IEEE) |
| publishDate |
2009 |
| url |
http://dspace.unimap.edu.my/xmlui/handle/123456789/7418 |
| _version_ |
1643788808471183360 |