A space-time-frequency analysis approach for the classification motor imagery EEG recordings in a brain computer interface task

We introduce an adaptive space time frequency analysis to extract and classify subject specific brain oscillations induced by motor imagery in a Brain Computer Interface task. The introduced method requires no prior knowledge of the reactive frequency bands, their temporal behavior or cortical locations. The algorithm implements an arbitrary time-frequency segmentation procedure by using a flexible local discriminant base algorithm for given multichannel brain activity recordings to extract subject specific ERD and ERS patterns. Extracted time-frequency features are processed by principal component analysis to reduce the feature set which is highly correlated due to volume conduction and the neighbor cortical regions. The reduced feature set is then fed to a linear discriminant analysis for classification. We give experimental results for 9 subjects to show the superior performance of the proposed method where the classification accuracy varied between 76.4% and 96.81% and the average classification accuracy was 84.9%.