Motor imagery ECoG signals classification using wavelet transform features

The input signals of brain computer interfaces may be either electroencephalogram (EEG) recorded from scalp or electrocorticogram (ECoG) recorded with subdural electrodes. It is very important that the classifiers have the ability for discriminating signals which are recorded in different sessions to make brain computer interfaces practical in use. This paper proposes an algorithm for classifying motor imagery ECoG signals, recorded in different sessions. Extracted feature vectors obtained with wavelet transform were classified by using k nearest neighbor method. The proposed algorithm was successfully applied to Data Set I of BCI competition 2005, and achieved a classification accuracy of 95% on test set.