Abstract
This paper presents an algorithm for classifying single-trial electroencephalogram (EEG) during the preparation of self-paced tapping. It combines common spatial subspace decomposition with Fisher discriminant analysis to extract features from multichannel EEG. Three features are obtained based on Bereitschaftspotential and event-related desynchronization. Finally, a perceptron neural network is trained as the classifier. This algorithm was applied to the data set (self-paced 1s) of "BCI Competition 2003" with a classification accuracy of 84% on the test set.
Publication types
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Comparative Study
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Evaluation Study
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Research Support, Non-U.S. Gov't
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Validation Study
MeSH terms
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Algorithms*
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Artificial Intelligence
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Cerebral Cortex / physiology*
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Cognition / physiology
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Computer Peripherals
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Databases, Factual
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Discriminant Analysis
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Electroencephalography / classification
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Electroencephalography / methods*
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Evoked Potentials, Motor / physiology*
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Fingers / physiology*
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Humans
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Imagination / physiology
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Models, Neurological
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Motor Cortex / physiology
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Movement / physiology*
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Pattern Recognition, Automated
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Principal Component Analysis
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Reproducibility of Results
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Sensitivity and Specificity
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Signal Processing, Computer-Assisted
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Somatosensory Cortex / physiology
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User-Computer Interface*