BCI Competition 2003--Data set IV: an algorithm based on CSSD and FDA for classifying single-trial EEG

IEEE Trans Biomed Eng. 2004 Jun;51(6):1081-6. doi: 10.1109/TBME.2004.826697.

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

  • Comparative Study
  • Evaluation Study
  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Algorithms*
  • Artificial Intelligence
  • Cerebral Cortex / physiology*
  • Cognition / physiology
  • Computer Peripherals
  • Databases, Factual
  • Discriminant Analysis
  • Electroencephalography / classification
  • Electroencephalography / methods*
  • Evoked Potentials, Motor / physiology*
  • Fingers / physiology*
  • Humans
  • Imagination / physiology
  • Models, Neurological
  • Motor Cortex / physiology
  • Movement / physiology*
  • Pattern Recognition, Automated
  • Principal Component Analysis
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Signal Processing, Computer-Assisted
  • Somatosensory Cortex / physiology
  • User-Computer Interface*