EEG Based Motor Imagery Classification Using Instantaneous Phase Difference Sequence

Brain-Computer Interfaces (BCI) are systems that enable users to use neural signals, typically Electroencephalogram (EEG) to direct an application or an external device. Motor imagery (MI) based BCI detects subject motor intentions which could be further used as control signals. Due to spatial lateralization of different MI tasks, spatial filtering followed by band power extraction is the most commonly used algorithm for classification tasks in MI-based BCIs. Unfortunately, the spatial filtering approach significantly incorporates Amplitude characteristics when compared to Phase characteristics of the EEG signal. Single trial Phase locking value (sPLV) has been a popular statistics to extract phase based information for classification task in MI-based BCI. To utilize the phase characteristics for MI classification, this paper proposes a novel approach based on instantaneous phase difference (IPD) sequence to extract phase features that explicitly use the phase synchronization information between EEG sensors. We maximized the discriminability of IPD sequence using linear transformation calculated from common spatial pattern algorithm (CSP) on the IPD sequence. An evaluation of our method on BCI competition dataset led to around 15% increase in mean classification accuracies compared to sPLV approach and comparable accuracies to power feature based CSP algorithm. Furthermore, incorporating phase features from our method and power features from traditional algorithms using sparse feature selection technique increased the classification accuracy over both CSP and CSP on the IPD sequence.