Neural network classification of EEG signals using time-frequency representation

This paper addresses the problem of classification of electroencephalogram (EEG) signals obtained from human subjects performing two mental tasks. One task named baseline involves relaxing and thinking of nothing in particular and the other task named multiplication involves mentally multiplying two...

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Bibliographic Details
Published in:Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005 Vol. 4; pp. 2502 - 2507 vol. 4
Main Authors: Gope, C., Kehtarnavaz, N., Nair, D.
Format: Conference Proceeding
Language:English
Published: IEEE 2005
Subjects:
Backpropagation algorithms
Condition monitoring
Electroencephalography
Feature extraction
Humans
Independent component analysis
Neural networks
Programming environments
Signal generators
Time frequency analysis
ISBN:0780390482, 9780780390485
ISSN:2161-4393
Online Access:Get full text
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Summary:This paper addresses the problem of classification of electroencephalogram (EEG) signals obtained from human subjects performing two mental tasks. One task named baseline involves relaxing and thinking of nothing in particular and the other task named multiplication involves mentally multiplying two 2-digit integers. First, the EEG signals are pre-processed using independent component analysis for removal of artifacts. Then, a time-frequency representation of the signals is generated, from which wavelet-based texture features are extracted for classification. The texture features are fed into a three-layer neural network classifier trained by the backpropagation algorithm. A classification rate of 96% is obtained for the dataset examined. The entire classification system has been implemented in the LabVIEW graphical programming environment providing a user-friendly interface to alter and monitor various parameters of the neural network classifier.
ISBN:0780390482
9780780390485
ISSN:2161-4393
DOI:10.1109/IJCNN.2005.1556296