BCI Competition 2003--Data set IV: an algorithm based on CSSD and FDA for classifying single-trial EEG.
other · Level V
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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.
Medical subject headings
- Algorithms
- Cerebral Cortex
- Electroencephalography
- Evoked Potentials, Motor
- Fingers
- Movement
- User-Computer Interface