Synchronization in brain (original) (raw)

2012, Hanieh Bakhshayesh, B.Eng. Honours Thesis, Flinders University

In this project, well-known synchronization measures (coherence, correntropy, cross correlation, phase synchrony and GFS) were compared to understand which one is the best measure for applying to EEG signals (electrical activity recorded from the human scalp). EEG is a nonstationary and noisy signal therefore a good synchronization measure should be insensitive to noise, be able to detect both linear and nonlinear relationships, and be able to detect nonstationary relationships. Time resolution and frequency resolution are other important properties of synchronization measures. Different approaches were considered to achieve the above requirements. Synchronization measures were applied to two types of simulated data: noisy sinusoidal signals with linear relationships and unidirectional coupled Hénon maps with nonlinear relationships. Different measures have both good and bad features. No measure is the clear winner but correntropy and phase synchrony shows promising results. The most appropriate synchronization measure must be chosen with knowledge of the application in hand. Additionally, the synchronization measures were applied to real EEG data. The first application was to find a synchronization measure which may be able to identify a persistent effect of disease in the brain. It is hypothesised that there are changes in the synchronization of brain signals of a patient with certain diseases, which are persistent across tasks. Each synchronization measure was evaluated for different subjects across different tasks. Phase synchrony and then coherence have the smallest dispersion from the mean (lowest standard deviation) and can be considered as the measures which are the least sensitive to changes due to the task. As phase synchrony also performed well on simulated data, it may be an appropriate measure to detect a persistent effect of disease in the brain. Thinking was the second application considered with real EEG. The main feature of thinking is its short time scale; therefore it is required to find a measure which is able to detect rapid changes in brain activities. To achieve this, all synchronization measures were applied to short blocks of EEG, sliding the block one sample at a time. There was no evidence that this analysis is able to distinguish EEG from noise. In another experiment, synchronization measures were applied to blocks of data of decreasing size, to identify measures that can detect synchronisation over very short timescales (i.e. thoughts) where synchrony is not apparent at long time scales. None of the results was in agreement with the expected outcome. As phase synchrony performed well on simulated data and robustly meets the null hypothesis of real EEG, in overall it can be concluded that it is an appropriate synchronization measure for further experiment on brain’s signal .but still it should be noted, application and aim of research are important aspects that needs to be consider for choosing the preferable synchronization measure.