The investigation of time-varying synchrony of EEG during sentence learning using wavelet analysis

Abstract

The synchrony analysis has been used as a tool for the purpose of investigating how the cognitive processes take place between different brain regions when the specified learning task is going on. We propose a novel method based on the time-frequency representation for quantifying synchronization between two channel EEG with both temporal and spectral resolution. The presented method employed the wavelet transform for cross coherent spectral analysis of the EEG signals recorded during sentences recognizing and learning. The wavelet-coherent magnitude spectra provide the information indicating the degree of coherence and the cross-wavelet phase relation serves to indicate the direction of information flow between two EEG channels on different cortical regions. Real EEG recordings are collected based on a cognitive target. It is observed from both the magnitude spectra and phase of the wavelet coherence that there are obvious differences between the identification of both Chinese and English sentences. These are helpful for the research on the English study for Chinese students.