Complexity Measure Applied to the Analysis EEG Signals

Electroencephalograms (EEGs) reflect the electrical activity of the brain. The problem of analyzing and interpreting the meaning of these signals has received a great deal of attention. Since EEG signals may be considered chaotic, chaos theory may supply effective quantitative descriptors of EEG dynamics and of underlying chaos in the brain. The complexity of the chaotic system can be characterized by complexity measure computed from the signals generated by the system. The complexity measures include the algorithm complexity of Kolmogorov and C₁/C₂ complexity. This paper gives one new complexity definition of partition algorithm complexity. The experiments proved that the method can distinguish health from diseases. Complexity measure not only provides a new method to analyze EEG signal, but also advances a new idea for diagnosing mental diseases