INVESTIGATION OF EEG NONLINEARITY IN DEMENTIA AND PARKINSONS-DISEASE

Many recent studies based on the surrogate data method failed to ident ify significant non-linearity in the EEG. in this study we examine whe ther the use of a different embedding method (spatial instead of time delay), and calculation of Kolmogorov entropy (K-2) and the largest Ly apunov exponent (L(1)) in addition to the correlation dimension (D-2), can distinguish the EEG form linearly filtered noise. We have calcula ted D-2, L(1) and K-2 of original EEG epochs and surrogate (phase rand omized) data in 9 control subjects, 9 demented patients and 13 Parkins on patients. The correlation dimension D-2 and the largest Lyapunov ex ponent L(1) could distinguish between the EEG tracings and the surroga te data. Demented patients had significantly lower D-2 and L(1) compar ed to controls. L(1) was higher in Parkinson patients than in demented patients. Contrary to other studies that have used the Theiler surrog ate data method, we find evidence for non-linearity in normal and abno rmal EEG during the awake/eyes closed state. Apparently it is the spat ial structure in the EEG that exhibits much of the non-linear structur e. Furthermore, non-linear EEG measures show more or less specific pat terns of dysfunction in dementia and Parkinson's disease.