EEG classification based on sparse representation

Classifying motor imagery brain signals where the signals are obtained based on imagined movement of the limbs is a major step in developing Brain Computer Interfaces (BCIs). Features from a small spatial region are approximated by a sparse linear combination of few atoms from a multi-class dictionary constructed from the features of the electroencephalography (EEG) training signals for each class. It is used to classify the signals based on the pattern of their sparse representation using a support vector classifier. The results show that the sparse representation classification algorithm is very effective for the pattern classification precision of the multi-channel EEG.