BCI Competition 2003--Data set IV: an algorithm based on CSSD and FDA for classifying single-trial EEG.
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.
Wang
Yijun
Y
Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China. wyj97@mails.tsinghua.edu.cn
Zhang
Zhiguang
Z
Li
Yong
Y
Gao
Xiaorong
X
Gao
Shangkai
S
Yang
Fusheng
F
D000465:Algorithms; D001185:Artificial Intelligence; D002540:Cerebral Cortex / Q000502:physiology*; D003071:Cognition / Q000502:physiology; D016287:Computer Peripherals; D016208:Databases, Factual; D016002:Discriminant Analysis; D004569:Electroencephalography / Q000145:classification / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D005385:Fingers / Q000502:physiology*; D006801:Humans; D007092:Imagination / Q000502:physiology; D008959:Models, Neurological; D009044:Motor Cortex / Q000502:physiology; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013003:Somatosensory Cortex / Q000502:physiology; D014584:User-Computer Interface