Variable Subset Selection for Brain-Computer Interface - PCA-based Dimensionality Reduction and Feature Selection.
Nuno Sérgio Mendes DiasM. KamrunnaharPaulo Mateus MendesSteven J. SchiffJosé Higino CorreiaPublished in: BIOSIGNALS (2009)
Keyphrases
- subset selection
- feature selection
- dimensionality reduction
- brain computer interface
- principal components
- principal component analysis
- motor imagery
- high dimensionality
- text categorization
- feature space
- high dimensional data
- low dimensional
- event related
- feature extraction
- signal processing
- dimension reduction
- classification accuracy
- support vector
- linear discriminant analysis
- evoked potentials
- principle component analysis
- computer screen
- eeg signals
- support vector machine
- event related potentials
- feature set
- eeg data
- brain signals
- unsupervised feature selection
- visuo motor
- brain activity
- healthy subjects
- genetic algorithm
- neural network
- spinal cord injury
- kernel pca
- sparse representation
- evolutionary algorithm
- pattern recognition
- learning algorithm
- machine learning