Analysis of EEG channel coupling for motor imagery applications

Abstract

The analysis of electroencephalographic (EEG) signals for motor imagery applications using the partial directed coherence (PDC) method highlights significant differences between channel pairs that correspond to movement and movement imagination brain activities. We improve the analysis based on PDC by decomposing the EEG signal into frequency components, in order to identify the frequency bands that are mostly influenced by motor imagery activity. We focus on the channel pairs Cz-FP1 and FP1-FP2 due to the fact that they show the highest difference when consider the PDC indicator mean values obtained for the recordings from the dataset. We compute the statistical difference between movement and movement imagery recordings by means of data dispersion and Student “t” test. These results are used to identify channels that are suitable for motor imagery application, in order to reduce the computational complexity of the brain computer interface (BCI) system and the corresponding algorithm.