The Wavelet-Coherence And Mutual Information Analysis Of EEG Signals

Electroencephalogram (EEG) during a cognitive process has attracted more attention in the past years due to its great practical and scientific significance. The basic properties of the neuronal system are information processing, transferring, and memorization, which are based on the extra cellular and intra cellular biochemistry reactions and electrophysiological activity. EEG has been widely accepted as the summation effect of the electrical activities of billions neurons on the scalp, which is full of physiological information within the brain. Experiments have shown the imprinting of the cognitive difference in the EEG recordings. We picked up the character difference during the different cognitive process by analyzing the EEGWe introduced coherent analysis to the EEG analysis. In this paper, because of the non-linearity of the EEG, we propose a novel method for time-varying coherent analysis of EEG The proposed method provides an effective way for quantifying synchrony with both temporal and spectral resolution. By using the continuous wavelet transform, wavelet spectrum is analysis from the EEG data. The wavelet magnitude spectra provide the degree of coherence and the wavelet phase serves to indicate the direction of information flow between two channels on different cortical regions. Besides, we researched the EEG coherence between different regions during the child recognized the Chinese and English sentences. Real EEG data is collected based on a cognitive target. It is observed from our research result that there are obvious differences of the mode of the information exchanged between the identification in Chinese and English sentences.Besides, we introduced information measure to evaluate the complexity evolution of EEG and the information transferring across the cerebral cortex during the cognitive process. To describe the interactions across the cerebral and inside the brain, we researched the mutual information (MI), which is estimated with adaptive portioning method and calculated the global mutual information during the sentences recognizing. The result also indicated that the mutual information has obvious differences between the identification in Chinese and English sentences. Finally, we use the EEG magnitude relief map to demonstrate the result.The results of our study indicate the possibility of using wavelet coherence and mutual information to study the EEG during cognitive process. There has obvious difference of the information exchanges during the Chinese and English sentences recognizing. These are helpful for the research on the bilingual learning in both English and Chinese for Chinese students.