Information Granule Reduction and Cluster based Partial least squares applied in EEG signals

EEG is a complex signal source, feature extraction and classification algorithm was studied for the brain electrical signal is also a key point in the research of brain waves, information granule clustering algorithm is one of the main idea, at the same time, the partial least square method is an effective method of dimension reduction, this paper, the use of information granule and partial least squares analysis of visual evoked potential EEG signals, the results show that this method can effectively extract the characteristics.