Common Cross-Spectral Patterns of Electroencephalography for Reliable Cognitive Task Identification

A current popular feature extraction method of classifying cognitive states and task engagements from electroencephalographs (EEG) is common spatial patterns (CSP). However, the classical CSP only focuses on the correlation between the signals and ignores all characteristics of the signals in the time domain and the frequency domain. In this paper we propose Common cross-spectral patterns (CCSP) a novel EEG feature extraction method for combining spectral and spatial patterns based on cross-spectral density (CSD) to overcome the disadvantages of classical CSP. In CCSP method. the cross-power matrices (CPMs) are extracted to measure the spatial correlation of each task in the band of interest. Then, an orthogonal linear transformation is constructed by simultaneously diagonalizing the CPMs of two tasks. Finally, each band’s logarithmic power of the transformed signals is extracted for the support vector machine (SVM) classifier. The experiment results on multiple datasets showed that the CCSP algorithm is fully applicable to multi-channel EEG for reliable multi-cognitive-task identification.