Exploring dimensionality reduction of EEG features in motor imagery task classification
This work analyzes feature selection and transformation methods for BCI systems. Three representative feature extraction methods are used: BP, Hjorth and AAR. An efficient LOO-CV technique is introduced for choosing the embedded dimensionality. Experiments have been conducted on five novice users during their first BCI sessions. According to its excellent results, LFDA is a promising method to design BCI systems.
dimensionality reduction motor imagery brain-computer interfaces electroencephalogram signals feature transformation linear discriminants local fisher discriminant analysis