Transition Detection and Sample Purification for EEG Based Brain Computer Interface Classification

This paper develops a novel method combining transition detection with the sample purification to filter noises in the raw EEG signal data, which helps to improve the precision of EEG based motor imagery classification. Note that the EEG samples belonging to the same class are time sequences across multiple electrodes, and these signals are in varying degrees contaminated by noise and artifact while also attention lapses by subjects during data acquisition. To overcome this problem, firstly, the transitions of EEG signals, the Euclidean distances between adjacent samples are larger than a given threshold, are extracted. Next, the sample purification is performed to filter the between-class noises based on the statistics of EEG signal. Finally, the purified EEG signals are treated as the input to the classifiers for BCI classification. Experimental results show that the proposed method is effective for the BCI competition III data (Data Set V), beating the winner.