Wavelet coherence for investigating the time-varying synchrony of EEG during sentence identification

In order to investigate how the cognitive processes take place in different brain regions, coherence analysis has been used as a tool for this purpose. This paper proposes a modern time-frequency method, which as an alternative for quantifying synchrony 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. The wavelet-coherent magnitude spectra serves to indicate the degree of coherence and the cross wavelet phase serves to indicate the direction of information flow between 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 Chinese and English sentences. These are helpful for the research on the English study for Chinese people.