Brain machine interface: Analysis of segmented EEG signal classification using short-time PCA and recurrent neural networks
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University of Basrah
2011
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my.unimap-113462011-03-21T08:39:32Z Brain machine interface: Analysis of segmented EEG signal classification using short-time PCA and recurrent neural networks Hema, Chengalvarayan Radhakrishnamurthy Paulraj, Murugesa Pandiyan, Assoc. Prof. Nagarajan, Ramachandran, Prof. Dr. Sazali, Yaacob, Prof. Dr. Abdul Hamid, Adom, Prof. Madya hema@unimap.edu.my Brain machine interface EEG signal processing Recurrent neural networks Link to publisher's homepage at http://www.uobasrah.edu.iq/ 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; the BMI uses 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 tasks 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. Two feature extraction algorithms using overlapped and non overlapped signal segments are analyzed. Principal component analysis is used for extracting features from the EEG signal segments. Classification performance of overlapping EEG signal segments is observed to be better in terms of average classification with a range of 78.5% to 100%, while the non overlapping EEG signal segments show better classification in terms of maximum classifications. 2011-03-21T08:39:32Z 2011-03-21T08:39:32Z 2008 Article Iraqi Journal for Electrical and Electronic Engineering, vol.4(1), 2008, pages 77-85 1814-5892 (Print) 2078-6069 (Online) http://www.ijeee.org/volums/volume4/IJEEE4PDF/paper7.pdf http://hdl.handle.net/123456789/11346 en University of Basrah |
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English |
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Brain machine interface EEG signal processing Recurrent neural networks |
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Brain machine interface EEG signal processing Recurrent neural networks Hema, Chengalvarayan Radhakrishnamurthy Paulraj, Murugesa Pandiyan, Assoc. Prof. Nagarajan, Ramachandran, Prof. Dr. Sazali, Yaacob, Prof. Dr. Abdul Hamid, Adom, Prof. Madya Brain machine interface: Analysis of segmented EEG signal classification using short-time PCA and recurrent neural networks |
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Link to publisher's homepage at http://www.uobasrah.edu.iq/ |
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hema@unimap.edu.my |
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hema@unimap.edu.my Hema, Chengalvarayan Radhakrishnamurthy Paulraj, Murugesa Pandiyan, Assoc. Prof. Nagarajan, Ramachandran, Prof. Dr. Sazali, Yaacob, Prof. Dr. Abdul Hamid, Adom, Prof. Madya |
| format |
Article |
| author |
Hema, Chengalvarayan Radhakrishnamurthy Paulraj, Murugesa Pandiyan, Assoc. Prof. Nagarajan, Ramachandran, Prof. Dr. Sazali, Yaacob, Prof. Dr. Abdul Hamid, Adom, Prof. Madya |
| author_sort |
Hema, Chengalvarayan Radhakrishnamurthy |
| title |
Brain machine interface: Analysis of segmented EEG signal classification using short-time PCA and recurrent neural networks |
| title_short |
Brain machine interface: Analysis of segmented EEG signal classification using short-time PCA and recurrent neural networks |
| title_full |
Brain machine interface: Analysis of segmented EEG signal classification using short-time PCA and recurrent neural networks |
| title_fullStr |
Brain machine interface: Analysis of segmented EEG signal classification using short-time PCA and recurrent neural networks |
| title_full_unstemmed |
Brain machine interface: Analysis of segmented EEG signal classification using short-time PCA and recurrent neural networks |
| title_sort |
brain machine interface: analysis of segmented eeg signal classification using short-time pca and recurrent neural networks |
| publisher |
University of Basrah |
| publishDate |
2011 |
| url |
http://dspace.unimap.edu.my/xmlui/handle/123456789/11346 |
| _version_ |
1643790138044579840 |
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13.159267 |