Analysis of time-varying synchronization of EEG during sentences identification

Abstract

The study of the synchronization of EEG signals can help us to understand the underlying cognitive processes and detect the learning deficiencies since the oscillatory states in the EEG reveal the rhythmic synchronous activity in large networks of neurons. As the changes of the physiological states and the relative environment exist when cognitive and information processing take place in different brain regions at different times, the practical EEGs therefore turn out to be extremely non-stationary processes. To investigate how these distributed brain regions are linked together and the information is exchanged with time, this paper proposes a modern time-frequency coherent analysis method that employs an alternative way for quantifying synchronization with both temporal and spatial resolution. Wavelet coherent spectrum is defined such that the degree of synchronization and information flow between different brain regions can be described. Several real EEG data are analysed under the cognitive tasks of sentences identification in both English and Chinese. The time-varying synchronization between the brain regions involved in the processing of sentences exhibited that a common neural network is activated by both English and Chinese sentences. The results of the presented method are helpful for studying English and Chinese learning for Chinese students.