JSD And LMCD Based Analysis Of EEG Signal

Electroencephalogram (EEG) signal can be analysised to accuratly predict brain activity anddiagnose health of human brain. Two new EEG signal analysis methods were put forward in thisthesis, the cores of the two algorithms presented were to classify and research EEG signals indifferent physical and emotional states with statistical complexity. This thesis mainly focused on thefollowing two aspects:Firstly, Lopez-Mancini-Calbet Divergence based complexity analysis of epilepsy abnormalelectroencephalogram. Complexity measure based on Lopez-Mancini-Calbet Divergence was usedto compute the statistical complexity of the basic rhythms β waves extracted from theelectroencephalogram signals, which include normal EEG and epilepsy abnormal EEG signalsduring a seizure separately. The results show that the β wave signals extracted from normalelectroencephalogram has the higher statistical complexity. The Independent Sample T Testindicated that above-mentioned analysis could disclose significant differences among these twosignals’ complexity. It has good reference for clinical detecting and epilepsy predication.Secondly, complexity analysis of electroencephalogram signal based on Jensen-ShannonDivergence. Statistical complexity measure based on Jensen-Shannon Divergence was used tocompute statistical complexity of the electroencephalogram signals, which include theelectroencephalogram of younger and elder subjects from Nanjing General Hospital of NanjingMilitary Command. The results show that two groups of signals have different statistical complexitymeasures. The electroencephalogram of elder subjects has the higher statistical complexity. Theindependent samples T test indicated that above-mentioned analysis could disclose significantdifferences among these two signals’ complexity. It is demonstrated that statistical complexity basedon Jensen-Shannon Divergence could effectively distinguish the electroencephalogram in2variousage groups.In order to achieve our research algorithms for clinical use, help doctors diagnose epilepsy, weachieve the above algorithm on android systems.