Automated detection of dynamical change in EEG signals based on a new rhythm measure

Automated detection of dynamical change in EEG signals has been a long-standing problem in a wide range of clinic applications. It is essential to extract an effective and accurate EEG rhythm indicator that can reflect the dynamical behavior of a given EEG signal. Time-frequency analysis is a promising method to achieve this end, but existing methods still have limitations in real implementation making this kind of methods still progressive until the present day. In this paper, along the line of ongoing research on time-frequency methods, we present a new method based on graph-based modeling. By virtue of this method, an effective and accurate EEG rhythm indicator can be extracted to characterize the dynamical EEG time series. Together with the extracted EEG rhythm indicator, an automatic analysis of continuous monitoring of EEG signal, is developed by means of a null hypothesis testing to inspect whether an EEG change occurs or not during a monitoring period. The proposed framework is applied to both simulated data and real signals respectively to validate its effectiveness. Experimental results, together with theoretical interpretation and discussions, suggest its promising potentials in practice.