EEG signal classification using PCA, ICA, LDA and support vector machines.
Abdulhamit SubasiM. Ismail GursoyPublished in: Expert Syst. Appl. (2010)
Keyphrases
- eeg signals
- support vector
- linear discriminant analysis
- principle component analysis
- principal component analysis
- feature extraction
- independent component analysis
- face recognition
- pca lda
- dimension reduction
- svm classifier
- subspace analysis
- dimensionality reduction
- feature space
- support vector machine
- classification accuracy
- signal processing
- small sample size
- eeg data
- learning machines
- principal components analysis
- support vector machine svm
- discriminant analysis
- svm classification
- extracted features
- subspace methods
- kernel methods
- linear discriminate analysis
- feature vectors
- feature selection
- large margin classifiers
- pattern recognition
- kernel function
- face images
- high dimensional
- brain computer interface
- support vectors
- gabor features
- independent components
- generalization ability
- independent components analysis
- high dimensional data
- generative model
- model selection
- neural network
- multi class
- covariance matrix
- positive and negative
- decision trees
- kernel density estimators
- radial basis function