Recognizing single-trial motor imagery EEG based on interpretable clustering method

Abstract

This work explores an approach for single-trial motor imagery (MI) electroencephalography (EEG) classification in interpretable clustering. The tensor structured EEG data under Mu rhythm is first processed by Common Spatial Subspace Decomposition (CSSD) to obtain the multi-dimensional CSSD-mapped EEG. In the dimensional feature reduction, Fisher’s ratio is used as the cost function to automatically find the optimal projection plane with two feature vectors of CSSD-mapped EEG corresponding to the largest Fisher’s ratio. Then, Discriminative Rectangle Mixture Model (DRMM) that gives a rectangular decision rule is used to identify optimal feature vectors to realize single-trial motor imagery EEG classification in an interpretable way. This innovative data analysis model generates reasonable cluster results driven by optimal feature data. The probability density distribution functions of EEG data in two classes can effectively explain the reliability of the rectangular decision rule given by the DRMM. The proposed method has been validated using the areas under the receiver operating characteristic curve (AUC) and cluster quality evaluation metrics. Experimental results demonstrate its performance is comparable to existing clustering and gives interpretable clustering results when detecting the motor intention involving EEG signals. This paper provides a novelty method based on interpretable clustering for single-trial MI EEG classification. And it may promote the development of BCI application.